blobpedia

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden of disclosing behavior. It explains that the tool walks a graph, returns prerequisite statuses, and accepts both merged CIPs and open-PR drafts. However, it does not explicitly state whether the operation is read-only, whether there are any side effects, or whether the traversal has limits (e.g., depth, cycle handling). These are useful but not critical gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, no filler. The action and output are front-loaded, followed by input formats and scope. Every sentence earns its place, and the structure makes the tool's purpose immediately scannable.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has no output schema and no annotations, the description covers the essential elements: purpose, input format, output, and applicable context. It doesn't detail edge cases like cycles, missing statuses, or max depth, but for a single-parameter tool this is a solid, usable description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema already documents cip_id with examples. The description adds semantic depth by explaining that 'CIP-0005' and '5' represent merged CIPs while 'PR-203' represents an open-PR draft, clarifying the input domain beyond the schema. This is valuable added meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb and resource ('Walk the CIP `requires:` graph'), states the exact output (full chain of prerequisites with statuses), and scopes itself to 'Celestia governance only,' which differentiates it from broader sibling tools like ecosystem_dependency_graph. An agent can clearly tell what this tool does and what it does not.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an explicit use case: 'Use when planning around a CIP that hasn't shipped yet.' This is a clear trigger condition. It does not mention when not to use it or name a specific alternative, but the context signal is strong enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full behavioral burden. It does this reasonably well by stating what is returned: counts per surface plus the most recent doc-page and forum statement. It could go further by clarifying how 'contradiction' is determined, but the core behavior is transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two compact sentences with no filler. The main action is front-loaded, and the output description follows immediately. Every word earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with no output schema, the description covers input and the key aspects of output well. It is slightly incomplete about what counts as 'GitHub' or how source scope is defined, but it is otherwise sufficient for an agent to invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the schema already describes 'topic' as 'Topic to cross-check.' The description adds surface context but does not meaningfully enrich the parameter semantics beyond what the schema provides, so the baseline applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Find') and a specific resource scope: contradictions between docs, forum, and GitHub on a topic. This clearly distinguishes it from sibling search/retrieval tools, which retrieve content rather than detect mismatches.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use case is clear: use this when you need to cross-check how different surfaces discuss a topic. It does not explicitly name alternatives or exclusions, but no sibling tool appears to offer the same cross-surface comparison, so the guidance is adequate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: input must be a literal error string or code, it returns top matches from two sources, and GitHub issues include dates. This is solid coverage for a read-only query tool, though it never explicitly states the operation is read-only, and the exact result encoding is left vague.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized at three sentences, front-loaded with the core action and output. Every sentence contributes useful guidance, including the negation and alternative. The only minor flaw is a slight redundancy where the final sentence ('Returns top matches from forum and github') repeats the sources already stated in the first sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

There is no output schema, so the description must explain return values. It does so only at a high level: 'top matches from forum and github' and 'GitHub issues (each with its own date).' It does not describe what fields a match contains, how 'resolved fixes' are formatted, or how the limit parameter affects the combined result. This is adequate for deciding to call the tool, but incomplete for an agent that needs to process the response.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, giving a baseline of 3. The description adds meaningful semantic nuance beyond the schema for error_text by clarifying that it must be a literal error string/code and by explaining why symptom descriptions are unsuitable, with a concrete counter-example and a pointer to semantic_search. The limit parameter is not mentioned in the description, but the schema already fully documents it.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Paste a Celestia ERROR MESSAGE, stack trace, or error code and get the most likely resolved fixes from blobpedia history.' It clearly names the output sources (forum threads and GitHub issues) and differentiates itself from the sibling semantic_search by contrasting its intended input. An agent can tell exactly what this tool does.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly says when to use the tool ('Needs a literal error string or code'), when not to use it ('do NOT use it for symptom descriptions with no error text'), provides a concrete counter-example ('my node won't sync'), and names the alternative tool ('use semantic_search for those'). It also scopes the tool to 'Celestia-specific' queries, giving unambiguous context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It discloses that the tool maps projects to mentioning repos and gives a quick reference picture, but it does not mention output format, direction of the dependency, or any limitations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, front-loaded with the core behavior and use case; 'Celestia-specific' is a useful scope qualifier. No filler, though the second sentence could be folded into the first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, the description is largely sufficient: it names the input, the transformation, and the user need. It could add return-shape details, but nothing essential to selecting or invoking the tool is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema already fully describes topic at 100% coverage, so the baseline is 3. The description adds a bit by explaining that the topic selects ecosystem_projects and that the output connects to GitHub repos, clarifying what the single parameter actually drives.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific operation ('Map ecosystem_projects matching a topic to the GitHub repos that mention them') with clear resources and output. The use case 'who else is building around X?' differentiates it from generic list/search siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It implies when to use the tool with 'Useful for...', and the Celestia-specific note gives context. However, it does not explicitly say when not to use it or how it differs from nearby siblings such as find_similar_projects or list_ecosystem_projects.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden. It discloses that searching happens over "titles and bodies" and that the tool is "Celestia-specific," which is helpful. However, it does not disclose output format, behavior when no match is found, or matching semantics beyond basic search.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two tight sentences with no filler. It front-loads the core action and scope, then gives a practical usage example.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter search tool without an output schema, the description is nearly complete: it states the input, the search domain, and the expected outcome. It does not mention edge cases like no matches, but this is minor given the tool's simplicity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the schema already describes the feature parameter with examples like "blob size limit" and "light client sampling." The tool description adds no additional meaning beyond what the schema provides, so the baseline of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb-resource pairing: "find the matching CIP(s)" by searching titles and bodies, and explicitly scopes it to Celestia. It also states the intended user question — "is there a CIP for X?" — which helps distinguish it from generic search tools and from get_cip/list_cips.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives a clear usage trigger: "Use to answer 'is there a CIP for X?'." This gives an agent an explicit condition for selecting this tool, though it does not mention exclusions or alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the behavioral disclosure burden. It clearly reveals the underlying filter logic, including the requirement for rendered HTML to contain `<pre>` or `<code>` blocks and match the topic keyword. It does not describe output ordering or exact returned fields, but the disclosed behavior is meaningful and not misleading.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The core action is front-loaded, and the second sentence adds the precise filtering behavior. Every clause earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple three-parameter tool, the description gives enough context about what is searched and how results are constrained. It does not detail the return shape, but there is no output schema and the expected output can be reasonably inferred as matching forum posts containing snippets.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description only reinforces that `topic` is a keyword, which the schema already states; no additional parameter semantics are provided. This is acceptable because the schema fully documents each parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Find') with a clear resource ('Celestia code snippets in forum posts') and adds a concrete matching criterion. It is easy to distinguish from generic siblings like search or semantic_search because it targets code-bearing forum content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The intended use case is clear: retrieve forum posts that contain code blocks and match a topic. It does not explicitly name alternatives or state when not to use it, but the specificity ('Celestia-specific' plus HTML filter) provides enough context for selection among siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the disclosure burden. It transparently labels the approach as a 'Lightweight heuristic' and explains the matching logic ('overlap of tags + adjacent categories'), signaling this is not an exhaustive or authoritative search. It stops short of describing output format or ranking, but the core behavioral caveat is present.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences deliver the purpose, scope, and heuristic with no filler. The main action is front-loaded and every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a one-parameter tool with full schema coverage and no output schema, the description supplies the input domain, the result concept, and the algorithm. It does not explicitly describe return value shape, but the tool is simple enough that the description is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already fully documents the single parameter with 'Project name or slug to look around.' The description adds useful restrictions by specifying that the project should be a single Celestia ecosystem project, narrowing the domain beyond the generic schema text.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific action ('find others'), a concrete resource ('Celestia ecosystem project'), and the result type ('complementary tags/category that could plug in'). This clearly differentiates it from the sibling find_similar_projects by emphasizing complementarity rather than similarity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides clear context for when to use the tool: when you have one Celestia project and need complementary collaborators. The 'Celestia-specific' note gives an explicit boundary, though it does not name alternatives or contrast with similar sibling tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of disclosing behavior. It does explain that ranking is based on forum activity matching the topic plus overall author volume, which is useful. However, it doesn't mention output format, potential empty results, or any limitations of the ranking heuristic.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with zero filler. The primary action and resource are front-loaded, and the ranking criteria are stated compactly. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter tool, the description is mostly complete for invoking it: topic name and optional limit are clear. But because there is no output schema, the description should explain what the ranked result looks like (e.g., author names, scores, counts); it doesn't. This is a notable gap, though not critical for basic invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters (limit and topic). The description adds no new parameter-specific details beyond restating that the topic drives forum matching, which is already implied by the parameter name and schema description.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Rank') and a specific resource ('likely Celestia experts on a topic across blobpedia'). It also reveals the ranking methodology (forum activity matching the topic plus overall forum volume), which makes its purpose distinct from sibling tools like search, find_similar_projects, or find_maintainer_guidance.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly establishes when to use this tool: when the user wants to identify experts on a topic within the Celestia ecosystem via blobpedia forum activity. It doesn't explicitly name alternatives or exclusion conditions, but the domain-specific phrasing ('Celestia-specific') and expert-ranking focus provide enough contextual signal to route an agent.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden, and it does so well: it reveals that only UNRESOLVED issues are returned, that results come from specific sources, that fixes/solutions are never returned, and that an empty response occurs when no open issue matches. It does not cover output format, authentication, or rate limits, but the core behavioral traits are disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences and dense with useful information. It front-loads the core purpose, then provides exclusions, empty-result behavior, and an alternative tool in a tight, well-ordered structure with no wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only two parameters, no output schema, and no annotations, the description covers the essential context: scope, source types, purpose, limitations, empty result behavior, and alternative routing. Nothing critical is missing for an agent to decide whether and how to call this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description reinforces that the input should be a 'free-text description' rather than a stack trace, but it does not add meaning beyond what the schema already provides for either the description or limit parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description gives a specific verb and resource: 'Surface known UNRESOLVED problems matching a free-text description'. It further disambiguates with concrete inclusion criteria ('forum threads with zero replies but high views, plus open GitHub issues') and explicit exclusions ('Does NOT return fixes, solutions, config, or how-to steps'), making it clearly distinct from siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent when to use this tool ('Answers "is anyone else hitting this?"') and when not to: 'for "how do I fix / configure / why does X happen" use semantic_search (then get_doc) instead.' The input schema also steers stack traces to diagnose_error, providing clear routing among alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full disclosure burden. It discloses the key selection behavior (top-volume contributors, likely maintainers/core team, Celestia-specific) and the intent of weighting expert voices. However, it does not explain how 'top-volume' is determined, what the return format is, or any limitations, leaving some behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The first sentence states the core action and resource; the second sentence gives a clear usage directive. Every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is simple: one parameter, no output schema, no nested objects. The description explains what it does, its scope, and when to use it. It could mention return shape or caveats, but for selecting and invoking this tool the description is largely sufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and the single 'topic' parameter is already well documented with an example. The tool description adds only the generic phrase 'on a topic,' which adds little beyond the schema. Baseline 3 is appropriate because the schema handles parameter semantics.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Surface') with a specific resource ('forum posts authored by top-volume Celestia forum contributors') and scopes it by topic. It distinguishes the tool from generic search by emphasizing the maintainer/core-team signal, so an agent can tell it apart from siblings like search or semantic_search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use it: 'Use when you want to weight expert voices over the general forum.' This clearly implies the alternative is general forum search, though it does not name a specific sibling tool or provide explicit when-not-to-use conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Since no annotations are provided, the description carries the burden of behavioral disclosure. It does reveal the heuristic keyword-matching mechanism and the probabilistic 'likely' nature, which is useful. Yet it omits return format, ordering, and false-positive caveats, leaving some behavioral ambiguity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler: the first states the purpose, the second explains the heuristic. Information is front-loaded and every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple read-only tool with one optional parameter and no output schema, the description covers the core purpose and behavior adequately. Missing items like explicit return details and sibling differentiation are minor given how simple the tool is.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema covers the single `limit` parameter 100% with a clear description and default. The tool description adds no parameter-specific meaning, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Surface') and a clearly scoped resource ('release notes likely to contain security or CVE fixes'). It also names the Celestia-specific heuristic, distinguishing it from general release-note tools like search_release_notes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The context is clear: use this when you need release notes that likely contain security/CVE fixes, and it explicitly scopes to Celestia. However, it does not name alternatives or provide explicit when-not-to-use guidance, so it stops short of full routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry behavioral disclosure on its own. It does reveal the matched fields and the Celestia-specific scope, which is helpful, but it omits how similarity is ranked, what the result set looks like, and whether there are any operational traits like pagination semantics beyond what the schema already states.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each earning its place: the first states the core action and mechanism, the second explains a concrete use case. No filler or redundant text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple three-parameter search tool with fully documented schema, the description is functionally complete: an agent knows the domain, the matching fields, and the suggested use case. It only lacks explicit alternative routing or a mention of result shape, which is a minor gap given the low complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with each parameter documented (description, limit, offset including defaults and bounds). The tool description adds no parameter-specific detail, but the schema does the heavy lifting, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description starts with a specific verb and resource ('Find Celestia ecosystem projects most similar to a free-text description') and clarifies the matching mechanism across 'title + category + description + tags.' This sharply distinguishes it from siblings like list_ecosystem_projects or semantic_search, so an agent can tell what it does without inspecting the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It states when it is useful ('before proposing a project to check overlap with what already exists'), giving clear contextual guidance. However, it does not explicitly name alternative tools or describe when not to use it, so the agent is left to infer routing among the many search/similarity siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses the returned content (release notes between versions plus nearby forum threads) and the Celestia scope, which is meaningful context. However, it doesn't disclose edge-case behavior (e.g., invalid or non-chronological versions, ordering of results) or the return shape, which is a gap given zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences with no filler: purpose is front-loaded, then usage trigger, then parameter format. Every sentence earns its place and the most decision-relevant information (what it returns, when to call it) comes first.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Reasonably complete for a 3-param tool with 100% schema coverage. Purpose, usage trigger, and version-format guidance are all present. The main gap is the uncompensated output shape (no output schema and no description of the exact return structure), which is a minor issue for a tool whose core output — release notes — is self-evident from the name and description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all three parameters are already documented in the schema. The description adds one genuinely useful rule — pass version strings as they appear in github_releases tags (with or without the leading 'v') — which slightly reinforces the schema's examples but doesn't go materially beyond them. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: 'Compare two versions of one Celestia repo and return the release notes between them, plus any forum threads near the release window.' The Celestia-specific scoping and two-version comparison make it conceptually distinct from siblings like get_recent_changes or search_release_notes, though it doesn't explicitly name any alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives an explicit trigger: 'Use when a developer is planning an upgrade and asks "what breaks moving from X to Y?".' This clearly frames when to invoke the tool. It lacks explicit when-not-to-use guidance or named alternatives, but the trigger condition is concrete enough for an agent to route correctly.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries burden. Indicates read-only operation ('Read'), and clarifies scope. Does not detail additional behaviors like auth or rate limits, but sufficient for a simple retrieval tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, no redundancy, front-loaded with primary action. Every sentence adds essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool is simple (one param, read-only, no output schema). Description fully covers purpose, scope, parameter source, and exclusions. Complete for an AI agent to use effectively.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers parameter fully (100% coverage), so baseline is 3. Description adds value by referencing prerequisite tools for obtaining the ID ('Get the id from search or list_cips first').

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states verb 'Read' and resource 'full text of one CIP by its id'. Distinguishes from siblings by specifying 'Celestia governance docs only — not GitHub issues or arbitrary proposals'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (CIP governance docs) and when not to (use GitHub tool for others). Provides guidance to get id from 'search or list_cips first'.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It implies a read-only list operation but does not explicitly state non-destructiveness or lack of side effects. The examples add context about attachment types, but behavioral traits like authorization requirements or rate limits are not mentioned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, 30 words, with zero wasted content. Each sentence serves a purpose: first defines the action and examples, second adds scope and parameter source.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple list tool with one parameter and no output schema, the description covers purpose, scope, parameter acquisition, and examples. It is slightly incomplete in not describing the return format or listing fields, but this is acceptable given the straightforward nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema has 100% coverage for the single parameter, providing format examples. The description adds value by telling the agent to obtain the id from sibling tools ('search or list_cips'), which is actionable guidance beyond the schema's scope.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'list' and the resource 'attachments of one Celestia Improvement Proposal (CIP)', with specific examples like 'signed vote records, supporting docs'. It distinguishes from siblings such as get_cip and list_cips by focusing on attachments and specifying governance scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit context: 'Celestia governance only' and 'Get the id from search or list_cips', guiding the agent on when to use and how to obtain the required parameter. However, it does not explicitly state when not to use or list alternative tools for other purposes.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the behavioral transparency burden. It correctly indicates a read-only operation ('Show the status-change timeline'), but does not disclose potential authentication requirements, rate limits, or any other behavioral traits beyond the basic action.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, front-loaded with the action, and contains no fluff. Every sentence adds necessary information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, but the description explains the output type (status-change timeline with examples). For a simple, single-parameter tool, this is sufficient. It does not mention pagination or limits, but that is acceptable given the scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the only parameter (id), so baseline is 3. The description adds marginal value by suggesting the id comes from search or list_cips, but the schema already describes the id format. The description does not significantly enhance parameter understanding beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool shows the status-change timeline of a CIP, with specific examples of transitions (Draft to Review to Final). This distinguishes it from siblings like get_cip (which likely shows overall CIP details) and list_cips (which lists CIPs).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description specifies that the tool is for Celestia governance only and requires a CIP ID obtained from search or list_cips. This gives clear context on when to use it, though it does not explicitly state when not to use it or mention alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. States what it does and required input, but does not disclose traits like rate limits, authentication needs, or behavior when no mentions exist. Acceptable for a simple lookup tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences: first lists sources, second adds context and prerequisite. Efficient with minimal redundancy, though the first sentence is slightly long.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter tool with no output schema, description adequately explains purpose, input, and sources. No mention of return format, but that is expected given no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 100% coverage describing the 'id' parameter format. Description adds value by explaining where to obtain the id (from search or list_cips) and referencing it in context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Specifies exact verb 'find' and resource 'references' across multiple sources. Clearly distinguishes from siblings like get_cip (retrieve specific CIP) and get_discussion (retrieve a specific discussion).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states 'Celestia-specific cross-reference — use to see who is discussing a CIP and where.' Mentions prerequisite (CIP id from search or list_cips). Does not explicitly state when not to use, but sibling list provides context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden of disclosing behavior. It clearly states this is a read operation ('Read') and specifies the scope ('a thread with its posts'). It also clarifies the source channel (forum.celestia.org vs GitHub Discussions). It doesn't mention error conditions or response format, but for a simple read-only tool, the behavior is sufficiently transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is just two sentences, with the core purpose in the first sentence and usage guidance in the second. Every word earns its place, no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's low complexity (single parameter, no output schema), the description covers the essential context: what it reads, where to get the id, and how to avoid confusing it with a sibling tool. It doesn't detail the return structure, but 'with its posts' implies that, and it's sufficient for a straightforward read operation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already provides 100% coverage for the single parameter 'id' (type integer, description 'Forum topic ID'). The description adds value by noting the id is numeric and providing concrete sources for obtaining it ('from search or get_cip_mentions'), which goes beyond the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: 'Read one Celestia community forum thread (forum.celestia.org) with its posts, by numeric topic id.' It uses a specific verb ('Read'), identifies the resource (Celestia forum thread), and distinguishes it from the sibling tool get_github_discussion.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly tells the agent when to use this tool versus an alternative: 'This is the CELESTIA FORUM channel — not GitHub Discussions (use get_github_discussion).' It also provides guidance on how to obtain the required id: 'Get the id from search or get_cip_mentions.'

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided; description carries full burden. It describes the tool as reading full text, implying read-only behavior. Does not explicitly state no side effects, but the description is sufficient for a read operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is three sentences, front-loaded with core functionality. No unnecessary words, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Tool is simple with one parameter and no output schema. Description explains what output contains (full commands, flags, sections) but doesn't specify return format explicitly. Adequate for a read-only tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has one parameter with description, coverage 100%. Description adds context that id comes from search/semantic_search, but does not add new semantic information beyond schema. Baseline 3 for high coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states it reads full text of a Celestia documentation page by id. It specifies the resource (Celestia docs) and distinguishes from siblings like search/semantic_search (which return snippets) and other docs tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use (Celestia operational/how-to questions) and when-not-to-use (not for other project docs, not for web). Advises prerequisite: get id from search or semantic_search first.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the burden of behavioral disclosure. It does not mention side effects, authorization, or data freshness, but it does clarify the tool's scope (people and org facts only), which adds some transparency. For a simple read-only tool, this is adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph that efficiently covers purpose, examples, and exclusions. It is well-structured and informative without being verbose. Minor improvement could be to separate usage guidelines more clearly.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, no output schema, and moderate schema coverage, the description provides a good overview of what is included and excluded. However, it does not describe the output format, which would help an agent interpret results. Overall, it is largely complete for decision-making.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50% (only 'topic' has a description; 'limit' lacks one). The description does not elaborate on parameters or their usage beyond the schema. For a tool with two parameters and low schema coverage, the description should compensate but does not, making parameter understanding reliant solely on schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides info on who runs Celestia, including leadership, team members, roles, background, and headline figures. It explicitly distinguishes itself from other tools by listing what it does NOT cover (documentation, CIPs, etc.), which is strong differentiation.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage examples (e.g., 'who is the CEO/CTO') and explicit when-not-to-use conditions with references to alternative tools (e.g., use search or get_doc for documentation). This fully satisfies the guideline dimension.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It reveals the cached-on-server behavior, the scope limitation to Celestia ids, and the fact that it returns full body plus comments. It doesn't detail error behavior for invalid ids, but the key behavioral boundaries are clearly disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is somewhat verbose but every sentence contributes: purpose, scope constraints, exclusions, and pairing advice. It is front-loaded with the main action and then clarifies boundaries. Slightly more concise phrasing would be possible, but the structure is logical and necessary.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a single-parameter read tool with no output schema, the description is complete: it states what it does, what input is valid, what to avoid, and how to obtain valid ids. It also mentions the return content (full body plus comments). No gaps remain for an agent to select and invoke correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema only describes id as a GraphQL node ID, but the description adds crucial semantics: the id must come from list_github_discussions, and raw GitHub node ids are invalid. This constraint is essential for correct usage and goes well beyond the schema, giving the agent necessary context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reads a single Celestia GitHub Discussion by id, including full body and comments, with clear scope (cached on this server, Celestia repos). It explicitly distinguishes from get_discussion (forum) and generic GitHub tools, so purpose is unambiguous and well-differentiated.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance: only for ids returned by list_github_discussions on this server. Explicitly states when NOT to use (raw GitHub node ids) and directs to alternative tools (dedicated GitHub tool, get_discussion). Mentions pairing with sibling tool list_github_discussions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It only states a repo scope and implies read-only behavior via 'Look up'; it does not disclose auth/visibility behavior, error handling for invalid or non-Celestia repos, or what status values are returned.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two short sentences with no filler, and the action and lookup key are front-loaded. The scope restriction is placed in a compact second sentence.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple two-parameter getter with a fully documented schema, the description is largely sufficient to invoke the tool correctly. It does not describe the return payload or when to prefer a sibling, but the tool name and 'status' imply the output, leaving only minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both parameters with examples. The description adds only the 'Celestia repos only' constraint, not new parameter-level meaning, so the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Look up'), names the resource ('one GitHub issue or PR'), and gives the lookup key ('repo + number'). It also adds a scope restriction ('Celestia repos only') that helps distinguish it from broader search or discussion tools in the sibling list.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear usage context: call this when you have a known repo and issue/PR number and need the issue's status. It does not explicitly name alternatives or say when not to use it, but the exact-match framing separates it from search_github_issues and get_github_discussion.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that exactly one release is returned, the kind of data returned, the Celestia-specific scope, and the need for a package input. It does not mention error behavior or rate limits, but for a simple read-only getter this is reasonably transparent.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two tight sentences with no filler. The core output and scope are front-loaded, and the constraint ('ONE NAMED Celestia repo') is explicit. Every sentence contributes useful information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description names the return fields, which matters because there is no output schema. The single parameter is fully documented in the schema. The only minor gap is clarifying behavior for unknown or non-Celestia package names, but for a low-complexity retrieval tool this is adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description adds little beyond the schema: it lists some allowed values and says a package name is required. This meets the baseline but does not add meaningful new semantic detail.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the action ('Get'), the resource ('the single latest release'), and the expected output fields (version tag, publish date, GitHub release-notes link). It also narrows the scope to a specific Celestia repo, distinguishing it from broader release/search tools like get_recent_changes or search_release_notes.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context: it is Celestia-specific, requires a package name, and works for one named repo among a small list. It does not explicitly state when to prefer an alternative tool, but the scope is precise enough to guide selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses real-time fetching from Celenium indexer and CoinGecko, specifies mainnet only, and lists all returned fields. No annotations provided, so description carries full burden; some missing details like latency or error handling.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with key purpose and sources. The list of fields is dense but necessary for completeness. Could be slightly shorter but not overly verbose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no parameters and no output schema, the description fully covers all returned data: block height, supply, validators, blob data, transactions, accounts, namespaces, TIA price, market cap, and 24h change. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Description confirms zero arguments and explains what fixed data is returned. With no parameters, baseline is 4; description adds value by listing all fields.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool fetches live Celestia on-chain state and TIA market data in real time, distinguishing it from the sibling tool get_network_stats which counts indexed content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says use this for current/live chain questions or TIA price right now, and contrasts with get_network_stats. Does not explicitly state when not to use, but the context is clear.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses that data is index/catalog coverage, not live chain state, which is critical for correct interpretation. With no annotations, description carries full burden and does well, though it could mention potential error scenarios or return format.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is very concise (two sentences) and front-loads the purpose, then adds distinction and alternative usage. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given zero parameters and no output schema, the description fully explains what the tool returns and what it does not, including a pointer to the sibling tool for related functionality. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters; description confirms 'Zero arguments.' Schema coverage is 100%, so no additional param details needed. Baseline 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states the tool returns counts of Celestia content indexed by blobpedia (CIPs, forum threads, docs, videos, etc.) and explicitly distinguishes from live chain state. This specificity differentiates it from sibling tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use this tool vs. alternative: 'For LIVE on-chain metrics... use get_network_state on THIS server instead.' Also notes zero arguments.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden and does well: it describes the content type (alerts with deadlines, affected components, source links), the filter levels, and sorting behavior (most severe and most recent first). It implies a read-only, safe operation without destructive side effects. While it doesn't explicitly state read-only or pagination details, the schema covers pagination, and the description provides substantial behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a compact three-sentence structure with no filler. It front-loads the core purpose, then filter capability, then usage triggers. Every sentence earns its place, and the flow is logical.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Complete for a relatively simple read-only tool. The description covers what the tool returns, how to filter, and when to use it. The input schema fully documents parameters, and no output schema exists but the description mentions the return fields (deadline, affected components, source link). Nothing essential is missing for an agent to call it correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, and the description does not need to elaborate on parameters. It only restates the level enum values, but the schema already provides full details for level, limit, and offset. The baseline of 3 applies because the schema handles the heavy lifting; the description adds minimal extra meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool retrieves the Celestia Radar, a curated feed of time-sensitive ecosystem alerts. It specifies the resource (Radar), the verb (Get), and differentiates it from siblings like get_breaking_changes or get_network_state. The scope is unique and immediately understandable.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit usage context: 'Use when a user asks what's changing, what's about to break, or what deadlines are approaching on Celestia.' It gives clear conditions for when to invoke, though it does not explicitly mention when not to use it or name alternative tools. Still, the guidance is actionable and specific.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It clearly states the return is a timeline of what changed, which is a read operation, but it does not explicitly disclose that it has no side effects, no permission requirements, or any pagination/rate-limit behavior. The description is adequate for understanding the operation, but lacks the behavioral detail that would be ideal for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with zero waste. The primary purpose is stated first, then the scope limitation, then routing to alternatives. Every sentence earns its place and the information is front-loaded. This is a model of concise, structured description.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with only two parameters, no required parameters, and no output schema, the description gives a good sense of the return type and scope. However, the absence of any mention of the parameters' semantics is a notable gap – an agent must infer what 'days' and 'limit' actually do. The overall picture is fairly complete for a read-only timeline tool, but the parameter documentation gap prevents a higher score.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, yet the description does not explain either the 'days' or 'limit' parameter. The defaults (7 and 50) are in the schema but the meaning – that days is the lookback window and limit is the max number of results – is not communicated in the description. An agent can guess from the names, but the tool should explicitly document these semantics since the schema itself has no descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly specifies the verb (get), the resource (recent changes across blobpedia), and the exact content scope (CIPs, forum threads, blog posts, GitHub releases, radar alerts). It goes beyond a generic statement by naming the sources and the ordering (newest first), and it differentiates from sibling tools like get_trending and GitHub-specific tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage guidance: it tells agents that this tool is for Celestia activity only, explicitly not GitHub commit feeds or general crypto news, and names the alternative tool (get_trending) for popularity-based queries. This is strong routing guidance that leaves nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the return content ('rendered release body with a link to the GitHub release') and the Celestia-specific scope, which is useful. It does not mention error cases, authentication, or rate limits, but for a read-only fetch tool this is a meaningful but not severe gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no filler. The opening 'Fetch...' immediately states the action and resource, and the second sentence adds useful return-value context. Every part earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description covers the essential elements: what is fetched, from which repos, for which version, and what the response looks like. It lacks explicit caveats like behavior for unknown versions or auth requirements, but for a simple two-parameter fetch with fully documented schema, this is nearly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters already described with examples ('Optional repo hint: app, node, core, rollkit, openrpc' and 'Version tag, e.g. "9.0.6", "v9.0.6"'). The description adds no new parameter-level detail beyond reconfirming the repo list and version target, so the baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: fetch the release-notes body for one version across five named Celestia repos. The 'Celestia-specific' qualifier clarifies scope, and the resource is concrete enough to be distinguished from generic release tools, though it does not explicitly name a sibling alternative.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies when to use the tool: when you need the release-notes body for a specific version. However, it does not explicitly state when to prefer this over siblings like get_latest_release or search_release_notes, nor does it provide exclusions or alternative routing.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden. It discloses the need to ask the user's background and return a path with documentation links. However, it does not specify the exact format of the response (e.g., whether it returns a list of links or a curated message) or any behavioral constraints like rate limits or required permissions. This is a minor gap given the tool's simplicity.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is three sentences long and front-loaded with the tool's purpose. Every sentence adds value: purpose, usage guidelines, and parameter instructions. While it is slightly verbose, the information density is high and no words are wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is complete. It covers the purpose, when to use, how to use (including the interaction pattern of asking background), and what to exclude. No additional information is needed for an agent to correctly select and invoke this tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with one parameter 'background' that has enumerated values. The description adds meaning beyond the schema by explaining that the parameter represents the developer's primary goal and instructs the agent to ask the user to pick one before proceeding. This guidance is crucial for correct tool invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides personalized onboarding for building on Celestia, with specific examples like running a node, posting a blob, or deploying a rollup. It distinguishes itself from siblings by explicitly recommending it over the Celestia search tool for getting-started questions.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('ALWAYS use this when a developer asks how to get started or build ON CELESTIA') and when not to ('NOT for getting started with non-Celestia frameworks'). It also provides a specific instruction to ask the user about their background first, and directs to defer to general tools for off-topic queries.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, but description clarifies it is a read operation (no arguments, returns trending content). Could mention that it does not modify any data, but the negative scope statements (NOT market trends, etc.) aid transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Four sentences with essential information, front-loaded with purpose. No fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the purpose and usage are clear, the description does not detail the output structure (e.g., list of titles, URLs). However, for a simple trending tool without output schema, this is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters, so schema coverage is 100%. Description explicitly states 'Zero arguments', reinforcing the absence. Baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool shows trending CELESTIA content on blobpedia, listing specific types (CIPs, forum threads, docs). It distinguishes from sibling tools like get_cip which target specific content.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (user asking about popular Celestia content) and when not (market trends, general web trends), with specific references to alternative MCPs for those cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full burden. It discloses that the tool reads from a server-side cache, does not fetch arbitrary YouTube videos, and returns both metadata and transcript. It does not explicitly describe missing-id or error behavior, but the main operational behavior is clearly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core operation, followed by scope exclusions and ID discovery guidance. Every sentence earns its place with no repetition or filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple id-based retrieval tool with two well-documented parameters, the description fully equips an agent: it states the return content (metadata plus transcript), the cache boundary, the exclusion of general YouTube search, and how to obtain valid ids. No output schema is needed because the return shape is adequately described.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents both id and transcript_chars thoroughly, including defaults, bounds, and the 0-for-metadata-only behavior. The description adds little beyond reinforcing the metadata-plus-transcript idea, which is already in the schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb ('Get'), a specific resource ('one Celestia-related YouTube video cached on this server'), and the key scope distinction (by video id, metadata plus transcript). It differentiates itself from generic YouTube fetching and from sibling tools like list_videos or search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It clearly states when to use the tool (for cached Celestia videos by id), when not to use it (arbitrary YouTube videos or searching YouTube at large), and provides explicit routing: use a dedicated YouTube tool for general video tasks, and find ids via list_videos or search.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description bears full burden. It states 'Read the full text', indicating a read-only operation without side effects. However, it does not specify the response format or any potential limitations like size.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, 30 words, no filler. Front-loaded with the primary action. Every sentence provides essential information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with one parameter and no output schema, the description completely covers purpose, limitations, and prerequisite. No gaps given the complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% (one parameter 'slug' described). The description adds value beyond the schema by explaining what a slug represents and advising to get a valid slug from list_whitepapers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Describes the action ('Read the full text'), resource ('one Celestia whitepaper or research PDF'), and how to identify it ('by slug'). Clearly distinguishes from siblings like get_doc and list_whitepapers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when not to use ('not arbitrary web PDFs') and suggests alternatives ('use a web-search tool'). Also provides a prerequisite ('Call list_whitepapers first to get a valid slug').

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations are absent, so the description carries the full burden. It does explain what the tool produces (a structured sequence of specific resource types) and that it is Celestia-specific, but it doesn't disclose the output format, whether it returns live or static data, or any side effects. For a non-destructive construction tool, this is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three short sentences, zero filler. The main purpose and sequence are front-loaded, and the sibling distinction is placed at the end. Every sentence contributes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one parameter, no annotations, and no output schema, the description is close to complete: it states the goal input, the four-phase sequence, and the Celestia-specific scope. It doesn't describe the exact return representation, which would be useful, but nothing an agent needs to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already describes the sole parameter 'goal' with examples, and coverage is 100%. The description adds only the phrase 'stated goal' plus the topic-driven framing, which does not materially extend what an agent already sees in the schema. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb ('Construct') and a precise resource ('structured Celestia onboarding sequence') for a stated goal, and enumerates the exact components (whitepaper, docs, CIPs, forum threads, talks). It also distinguishes itself from the sibling get_started_guide, so an agent can tell the tools apart without inspecting schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It explicitly contrasts this tool with get_started_guide: 'Topic-driven (distinct from get_started_guide, which is background-driven).' This states the condition for choosing this tool over the primary alternative, which is explicit when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the behavioral burden. 'Browse or filter' clearly implies a read-only listing operation, and the statement 'Use get_cip to read one in full' signals that list results are likely summaries, not full CIP content. It does not mention pagination, ordering, or rate limits, but those are not critical for a simple list/filter tool and no contradictory annotations exist.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, each earning its place: the first states what the tool does, the second provides critical exclusions and fallback tools, and the third routes to sibling tools for related operations. There is no fluff or redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description covers scope, filtering dimensions, exclusions, and sibling routing well. It does not describe the exact result shape or default pagination behavior, but the schema provides limit/default details and the 'read one in full' hint helps set expectations about list output. Slightly more detail on the default status/all behavior would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33%, so the description must compensate. It does clarify the two main filter dimensions, 'status' and 'type', and gives examples like Draft/Review/Final for status. However, it omits the `limit` parameter entirely and the status examples are far from exhaustive relative to the 17-value enum, so it does not fully compensate for the sparse schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb ('Browse or filter') and resource ('Celestia Improvement Proposals (CIPs)'), and immediately differentiates itself from related tools by scoping to Celestia governance proposals and explicitly excluding GitHub issues, PRs, and other-chain proposals. This makes the tool's role unambiguous even before seeing sibling names.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-not-to-use guidance ('do NOT use this to list GitHub issues, pull requests, or proposals from other chains') and names the alternative category (GitHub MCP or that chain's tool). It also routes to the relevant siblings for related needs: get_cip for full reads, get_cip_history for status timelines, and get_cip_mentions for discussions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the burden. It discloses 'Returns catalog entries, not live project data,' implying read-only, but lacks detail on auth, rate limits, or reversibility.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose, no fluff. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, so description should cover return values. It says 'catalog entries' but not structure or fields. Missing pagination details, though limit implies some. Adequate for a simple browse tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67% (query and category described). The description reiterates these filters but adds little beyond 'optional filtering.' The limit parameter is not mentioned but has schema details.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool browses the Celestia ecosystem catalog (rollups, DeFi, etc.) and explicitly distinguishes it from a general crypto directory, differentiating it from sibling tools like get_cip or search.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when to use (Celestia ecosystem) and when not to (use a web-search tool for general projects), providing an alternative.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses data source (cached), ranking order, and scope. No annotations provided, so description carries burden; it adds context about caching and ranking though omits potential staleness or authentication needs.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with core purpose and key constraints. No unnecessary details, every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Covers purpose, scope, parameters, and alternatives. Lacks output format details, but acceptable for a list tool with no output schema. Adequate for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Adds meaning to two parameters with examples (e.g., 'celestiaorg/docs', 'Q&A'). Explains ranking context. Despite high schema coverage, description provides useful usage hints beyond schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it lists GitHub Discussions from Celestia repos cached locally, ranked by upvotes then recency. Distinguishes from get_github_discussion (single thread) and get_discussion (community forum).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly limits to Celestia repos and local cache, warns not for arbitrary GitHub Discussions, and directs to dedicated tools. Provides clear when-to-use and alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

States 'Read-only view' and 'Zero arguments', indicating safe, simple operation. With no annotations, this covers the basic behavioral profile, but lacks detail on output format (e.g., structure of item counts).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with action and scope, no extraneous words. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Describes scope and limitations well for a simple list tool. However, without an output schema, the description could be more complete by hinting at the return format (e.g., list of repo names with counts).

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Zero parameters and 100% schema coverage. The description adds value by explicitly noting 'Zero arguments', confirming no inputs needed. Baseline of 4 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the verb 'list', the resource 'GitHub repositories', and specifies the scope: 'that blobpedia has INDEXED for Celestia, with item counts'. It distinguishes from sibling tools by contrasting with browsing GitHub accounts or arbitrary repos.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly tells when not to use ('does NOT browse your GitHub account or arbitrary repos') and provides an alternative ('use a dedicated GitHub tool for that'). Clearly defines its limited context.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses it's a server cache, metadata-only, and doesn't search YouTube. While read-only behavior is implied, explicit 'read-only' would improve. Still, it provides enough behavioral context for safe use.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tightly-coupled sentences: purpose, limitations, and pointer to sibling. No fluff, front-loaded with main action, and every sentence adds value. Excellent structure.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema given, so description should cover return value. It states 'metadata only' but doesn't specify which metadata fields are returned. With 5 parameters and no output schema, this is a notable gap. However, the description is otherwise informative within its scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 60% with descriptions for some parameters. Description adds high-level filter categories (channel, date, transcript) but doesn't detail specific parameter formats or constraints beyond what schema already provides. Adds some context but not substantial new meaning.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists Celestia-related YouTube videos from a cached server, with filters. It uses a specific verb 'Browse' and resource 'YouTube videos cached on this server', distinguishing it from siblings like 'get_video' and general YouTube search tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly states when not to use: 'does NOT search YouTube at large' and directs to 'use a dedicated YouTube tool for that'. Also points to 'get_video' for transcript retrieval, providing clear alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, and the description does not disclose behavioral traits such as authentication, rate limits, or side effects. It does describe the return format but lacks further context for a low-risk list operation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise with two sentences, front-loaded with purpose, and every sentence adds essential information without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema and zero parameters, the description fully explains what the tool lists and how to proceed for details, making it complete for a simple list tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

No parameters exist; the description adds value by implicitly stating the output includes slug and title, which is sufficient for a parameterless tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it lists Celestia whitepapers and research PDFs, specifying output includes slug and title, and explicitly distinguishes itself from tools for arbitrary web PDFs.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance: use for Celestia-specific PDFs, not for arbitrary web PDFs (pointing to web-search tools), and mentions the sibling tool get_whitepaper for reading one by slug.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It indicates the tool does exact-term/full-text search (not semantic), which is helpful. However, lacks details on pagination, result format, or potential side effects. Still, the behavior is clear enough for safe selection.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Description is reasonably concise given the complexity. It front-loads the main purpose and then provides usage guidance. Two sentences dedicated to what not to use, which is valuable but could be slightly tighter.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (many sources, many sibling tools), the description is highly complete. It explains scope, alternatives, and when to use other tools. No output schema, but search results are typically self-explanatory.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 33% (only query described). Description adds context by listing query sources implicitly but does not detail the 'type' enum or 'limit' parameter. Partially compensates for low coverage but not fully.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs keyword/full-text search over a specific knowledge base, listing sources. It distinguishes itself from siblings by specifying exact-term vs semantic search (semantic_search) and page retrieval (get_doc).

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly provides when-not-to-use guidance: not for other blockchains, general web, other knowledge bases, or local files. Also directs to alternatives like semantic_search for conceptual questions and get_doc for full page reading.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

There are no annotations, so the description carries the burden of disclosing behavior. It does disclose that the search is full-text, limited to Celestia ecosystem, and over an index ('blobpedia's indexed GitHub issues'). However, it does not state the result shape, default filtering behavior, or whether the index is live; those are left to the input schema or implied by 'search.'

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no filler: scope is front-loaded, use cases are in the second sentence, and the alternative is in the last. Every sentence contributes and the text is compact.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool is a simple 4-parameter search with one required parameter, and the schema fully documents each parameter. The description covers scope, use cases, and the main sibling distinction; the only minor gap is not describing the return shape, but for a full-text search this is largely self-evident.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the input schema already documents all four parameters. The description adds no parameter-specific detail beyond the general query semantics; baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description begins with a specific verb and object: 'Full-text search across blobpedia's indexed GitHub issues across Celestia repos.' It clearly identifies the corpus and ecosystem scope, and the final sentence distinguishes it from the broader `search` tool, so it does not depend solely on the tool name.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It directly states the intended use: 'Use to find prior reports, open bugs, or requested features.' It also gives an explicit alternative and condition: for broader cross-corpus search, use `search` with type='github'. This is actionable guidance rather than inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description must carry behavioral disclosure. It communicates a read-only search across all indexed Celestia repos and implies release-level results, but it does not describe matching semantics, result structure, ordering, or potential limitations. This is adequate but not rich.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, each earning its place: the first states the action and scope, the second states the intended use case. No filler or redundant details.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with one simple, fully documented parameter, the description covers scope and purpose well. However, since there is no output schema, a bit more detail about what the returned results look like would make it fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The single parameter 'feature' is fully described in the schema with examples. The description adds only the phrase 'feature/keyword', which aligns with the schema but does not provide additional semantic value beyond what the schema already offers.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

Description names a specific action ('Search'), a specific resource ('release-notes bodies of all indexed Celestia repos'), and a clear intent ('find which release first mentioned X'). This distinguishes it from generic siblings like search and semantic_search by scope and domain.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides a clear use case: use when you need to know which release first mentioned a feature or keyword. It does not explicitly list exclusions or alternative sibling tools, but the context is sufficient for most agents.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the disclosure burden. It transparently reveals the indexed data source, that matching spans title and transcript, and that results include a short snippet around the hit. The read-only nature is implied by 'search' and 'returns', though no explicit safety or rate-limit details are provided.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two tight sentences with no filler: it front-loads the purpose, adds domain scope, and then states the matching and snippet behavior. Every sentence earns its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple 3-parameter search tool with no output schema, the description provides the essential context: what is searched, the domain scope, which fields are matched, and what the returned results look like. It does not specify full result fields or ordering, but nothing required to invoke it correctly is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% and all three parameters (query, limit, offset) already have meaningful descriptions. The tool description adds little parameter-specific meaning beyond confirming title/transcript matching, which is also already in the schema, so the baseline score of 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses a specific verb ('Search') plus a clearly defined resource ('blobpedia's indexed Celestia talks/videos (YouTube transcripts)') and states the match scope ('title + transcript') and output type ('short snippet'). This distinguishes it from generic search siblings by emphasizing its Celestia-specific video/transcript focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives clear context by stating it is Celestia-specific and searches indexed talks/videos, which helps an agent choose it for video-content discovery over broader search tools. It does not explicitly name alternatives or state when not to use it, but the scope is clear enough for correct selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses the semantic nature of the search, the Celestia-specific scope, and implicit non-return expectations by pointing to get_doc for full pages. It could go further by describing result shape, ranking, or absence of side effects, but the behavioral characterization is strong for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense and front-loaded with the core semantics and resource scope, then efficiently covers exclusions and alternatives. It is long, but nearly every clause adds routing or scoping value. It could be slightly better structured into separate sentences, but it avoids redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The description thoroughly covers scope, use case, and alternatives, which is critical given the long sibling list. However, with no output schema, it does not explicitly describe the result format, even though it implies results contain ids via the get_doc reference. It also does not explain limit behavior or how source_types filtering affects results, leaving some practical gaps for an agent invoking the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 67%, so the description bears some responsibility for parameter meaning. It adds useful context to the query parameter by framing it as conceptual how-does-X-work phrasing, but it does not elaborate on 'limit' or 'source_types' beyond what the schema already states. This is adequate but not exceptional.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies a semantic search tool over the Celestia knowledge base, enumerating the document types it covers. It explicitly distinguishes itself from sibling tools by stating that exact-term/name lookups should use 'search' and full-page reads should use 'get_doc', which fully disambiguates it from the many search/get siblings.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: conceptual how-does-X-work questions. It also provides strong negative guidance, listing concrete alternatives for other blockchains, general web, other knowledge bases, and local files, and names the sibling tools that should be used instead. This is exemplary routing behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.