changegamer

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

With no annotations, description fully discloses return fields (valid flag, tier, creation time, slugs, grant) and explicitly states what is never returned (key, email, Stripe session).

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, front-loaded with purpose, no redundancy, 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?

Complete for a simple tool with one parameter and no output schema; adequately explains return behavior and constraints.

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 with clear description of api_key parameter; description adds only minor usage context, meeting baseline.

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 verifies an access key and reports what it unlocks, distinguishing from sibling tools like get_access_info and get_corpus.

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 recommends use for confirming a freshly-purchased key, implying when to use but not explicitly excluding 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 the description bears full burden. It indicates a read operation ('Return') but offers no details on authentication, rate limits, or what fields are included. Lack of output schema further reduces 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?

Single sentence, front-loaded with the verb and resource. Efficient but could be slightly expanded without losing conciseness.

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?

With no annotations, no output schema, and no parameters, the description is minimal. It lacks details on the format or structure of the returned information, which would help an agent interpret the tool's output.

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, so schema coverage is 100%. The description adds no parameter info, but none is 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?

The description clearly states the verb 'Return' and the resource 'access and pricing information for ChangeGamer resources', making the purpose specific. It distinguishes from siblings like check_access and get_pricing by indicating this tool combines both types of information.

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?

No guidance on when to use this tool versus alternatives. Without explicit context or when-not-to-use instructions, an agent might not know whether to pick this over check_access or get_pricing for combined vs separate info.

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 does add meaningful behavioral context: the result is always free, not drawn from the paid corpus, and the response form is full Markdown with a metadata header. It leaves out failure behavior for unknown slugs, any auth/access requirements, and additional disclosure around content and formatting limits, which keeps it from a 4.

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 crisp sentences, front-loaded: the core fetch behavior first, then the free-access clarification, then the slug-discovery routing. No filler, no redundancy, and every clause carries an actual detail needed for correct invocation.

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 nested objects and no output schema, the definition fully covers the core contract: which slug to pass, what you receive back, and the free/paid access area. The only notable gap is unspecified behavior for a non-existent slug, which would likely be a 404, but the simplicity of the call keeps this from being a serious deficiency.

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 gives 100% parameter coverage, including a concrete example (rag-in-production), so the baseline is 3. The description adds one genuinely useful layer by telling the agent how to obtain a valid slug (via list_articles and the same discovery path). That is helpful direction, but it does not add syntax, format, or constraint details beyond the existing 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?

States a specific verb (fetch), a precise resource (one ChangeGamer guide / editorial article), the selection key (slug), and the return format (full Markdown plus metadata header). The restrictor one plus the qualifier editorial article cleanly distinguishes it from list_articles and get_resource without requiring any schema inspection.

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 to use list_articles to discover slugs, which gives the agent a concrete workflow and an explicit alternative. The statement always free, never part of the paid corpus tells the agent this call is the right one for free editorial content, not a paid paywall request. It does not exclude other siblings (e.g., get_resource, get_corpus), so routing is strong for one alternative but not exhaustive.

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 present, so the description carries full burden. It does substantial work: clarifies it returns a structured bundle (pillar metadata, sub-articles with variant URLs, full Markdown body) and specifies the free editorial tier. Minor gaps remain (no error/not-found behavior, no auth requirements spelled out), but the read-only, compositional nature of the call is well conveyed.

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-loads the what (fetch a cluster bundle), details the return composition in one compact clause, and closes with a practical discovery tip. Every sentence earns its place; no redundancy 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?

Although there is no output schema, the description enumerates the three main return elements (pillar metadata, sub-articles with variant URLs, full Markdown body), so the agent knows what to expect. The required parameter and its example are covered by schema. Missing aspects are minor for a get-by-id tool: no explicit statement of error behavior for invalid cluster ids or indication of payload size, but the scope is well defined.

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 a concrete example ("rag-in-production"), and the description adds provenance guidance ('extract from editorial layer,' 'Use listarticles to discover ids'), clarifying what the cluster_id identifies beyond the schema's 'Cluster id.' Since there is a single parameter and the cost of misuse is low, this is strong value above 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?

States a specific verb (fetch) and resource (ChangeGamer guide cluster/topic bundle) and enumerates the exact payload: pillar metadata, sub-articles with variant URLs, and the full Markdown body. This clearly differentiates it from siblings like get_resource and list_articles because it maps to a distinct 'cluster' concept with a defined composition.

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 the agent how to source a valid input ('Use list_articles to discover cluster ids') and scopes the tool to the 'free editorial layer,' which implies when no premium/access checks are needed. It does not explicitly state when NOT to use this tool, but the discovery hint and layer scoping give clear 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 provided, the description clearly discloses the tool's behavior: it returns full content for free resources and stubs for premium resources, and notes the large response size. This sufficiently informs the agent of what to expect, though it could mention potential rate limits or timeout considerations.

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 with no waste. The first sentence states the purpose and scope, the second clarifies premium behavior, and the third gives a usage recommendation. It is front-loaded and efficient.

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 no parameters, the description adequately covers what the tool returns and important caveats (large size, premium stubs). It could be slightly more complete by mentioning format details (e.g., that content is in Markdown), but overall it is sufficient for an agent to use the tool 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 tool has no parameters, and the schema coverage is 100% (vacuously). The description adds value by explaining what the single output contains and how the response is structured, which is beyond the schema's empty definition.

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 returns the entire free corpus as one document, specifying exactly what is included (titles, descriptions, URLs, full Markdown bodies) and how premium resources are handled (stubs with purchase links). This distinguishes it from siblings like get_resource or list_resources, which have narrower scopes.

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 advises using this tool to 'ingest everything in a single call' and warns that 'the response is large', providing clear usage context. It does not explicitly state when not to use it, but the existence of siblings like get_resource implies alternatives for targeted access.

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, description fully discloses authentication requirements, potential upgrade object return, and that it's a single document. Includes key format (cg_...).

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, front-loaded with purpose, then conditions. No unnecessary 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?

Given only one parameter and no output schema, the description covers purpose, prerequisites, failure case, and alternative tool, making it complete for effective use.

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 already describes api_key as 'Corpus/Enterprise license key (cg_...)', but description adds context about entitlement requirements, Starter key insufficiency, and failure mode. Adds value 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?

Description clearly states it returns the entire corpus including premium resource bodies, identifies it as the key deliverable of the Corpus/Enterprise license, and distinguishes from sibling get_corpus (free, premium-stubbed version).

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 specifies when to use (Corpus/Enterprise key required), what happens if not entitled (payment-required object returned), and mentions alternative get_corpus for free access.

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 fully carries the burden. It clearly indicates a read-only operation by stating it 'returns' data and references a JSON endpoint. No destructive or side effects are implied, and the mention of the retry loop adds transparency about potential HTTP behavior.

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-loading the core purpose in the first sentence and adding a reference for consistency in the second. Every word is informative with 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?

For a parameterless tool with no output schema, the description is self-contained. It covers all relevant aspects: payment methods, retry loop, free items, recommendations, and data source. Nothing essential 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 tool has zero parameters, so schema coverage is 100% and no parameter documentation is needed. The description adds value by explaining what the output contains, which is beyond the schema's empty 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 explicitly states the tool returns the agent payment manifest, detailing payment methods, retry loop, free items, and recommended paths. It distinguishes from siblings like 'get_pricing' by focusing on the full payment manifest and referencing a specific API endpoint.

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 use for exploring payment options and provides a recommended path per use case. However, it does not explicitly contrast with sibling tools like 'get_pricing' or 'check_access', missing some clarity on when to choose this tool over others.

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 explicitly lists the return elements (tiers, price, currency, interval, etc.) and notes it's the same as /api/pricing.json. However, it does not disclose whether the data is cached or if it requires authentication, but for a read-only, zero-parameter tool, the behavioral detail is sufficient.

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 concise sentences that front-load the main purpose and provide a helpful reference to the equivalent API endpoint. No wasted 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?

Given no output schema, the description compensates by enumerating the return fields (price, currency, interval, etc.). It could mention that the data is static or note any limitations, but for a simple catalog tool, it is largely complete. The sibling list is provided but not differentiated, which is acceptable.

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?

There are no parameters in the input schema, so the description has no need to add parameter semantics. Baseline 4 applies since the schema coverage is 100% and no parameters exist. The description correctly omits parameter 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 returns the full paid-offer catalog with specific details (price, currency, interval, etc.). It distinguishes itself from siblings by noting it provides the same data as /api/pricing.json, making the purpose unmistakable.

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 usage for obtaining the complete pricing catalog, similar to a specific API endpoint. While it doesn't explicitly state when not to use it or provide alternatives, the context of siblings like get_payment_info suggests this is for the full catalog rather than individual records. Clear but lacks explicit exclusions.

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 full burden. It discloses key behaviors: free resources return full metadata and Markdown body, premium resources require a valid api_key, and without it a payment-required object is returned. This adds value beyond the schema, though it omits error handling and response structure details.

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 unnecessary words. The information is front-loaded and each sentence is essential.

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 conditional behavior and no output schema, the description adequately explains the two cases (free/premium). It doesn't include error scenarios or explicitly reference sibling tools, but the provided information is sufficient for basic usage.

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 baseline is 3. The description adds behavior context for the api_key parameter, explaining its role and consequences of omission, which is valuable beyond the schema's simple 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 clearly states the tool fetches a ChangeGamer resource by slug, specifying the verb 'Fetch' and the resource. It distinguishes between free and premium resources, setting it apart from sibling tools like list_resources and search_resources which operate differently.

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 this tool (when you have a slug) and provides context for free vs premium resources. However, it does not explicitly mention alternatives or when not to use it, relying on the distinct slug parameter for differentiation.

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 behavioral transparency burden. It does disclose useful behavior beyond the name, such as the editorial block being 'merged at runtime from the static articles index' and the freshness/size signal purpose. However, it does not describe any side effects, auth requirements, or response format. Overall, it gives moderate, but not complete, behavioral insight.

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 economically structured: a single dense leading sentence enumerating the content, followed by one short sentence stating the intended use case. There is no fluff, and the most distinctive features are front-loaded in the list.

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 absence of parameters and output schema, the description is nearly complete for an agent deciding how to invoke it. It specifies both what is included and why it would be used. It could be strengthened by naming the sibling tools it is not, but the provided context is enough to understand the tool's role.

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 tool has zero parameters, so the baseline score is 4. The description does not need to add parameter information because the schema already contains no properties. The description goes a step further by clarifying the kind of detailed output the tool produces, which is more than sufficient for a no-parameter 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 opens with a specific verb and object, 'Return corpus stats', and then enumerates a detailed breakdown: total/free/premium counts, per-category counts, tag count, dates, recent updates, feed URLs, and an editorial block. This clearly distinguishes the tool from siblings like get_corpus and get_full_corpus because the focus is aggregated statistics, not corpus 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 description provides explicit context for when to use this tool: 'A small freshness/size signal to poll before deciding whether to re-ingest.' It clearly communicates the polling use case, though it does not explicitly name alternatives or exclusions. This is solid practical guidance with only minor ambiguity about how it compares to other list/resource 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, the description carries the full behavioral burden and does a solid job: it explains the result is a hub/spoke cluster graph, enumerates included fields, warns that article bodies are absent, and notes the content is always free. It does not cover pagination, ordering, or response size, but zero-parameter full listing makes those less critical.

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 dense sentences, with the primary purpose front-loaded and every clause carrying relevant information. The field list and body-content caveat earn their place without padding.

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?

With no output schema, the description compensates by listing the exact available fields and the body-return exclusion, and it provides enough direction to fetch bodies elsewhere. Minor ambiguity remains in what 'cluster-first hub/spoke graph' means structurally, but an agent can still invoke the tool successfully.

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 tool has zero parameters, so there is nothing for the description to clarify beyond confirming no required inputs. Baseline 4 applies because no params exist.

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 identifies the tool as listing editorial guides, states the resource is ChangeGamer's free editorial corpus, and differentiates it from the licensable corpus and from get_article body retrieval. It names the specific output fields, so an agent knows the exact resource and 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 explicitly says this tool returns metadata only and directs the agent to get_article for body content. It also separates the editorial guides from the licensable corpus, so an agent can decide around other corpus-listing siblings. It does not explicitly enumerate all sibling alternatives, but the exclusions given are substantive.

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?

Provides key behavioral trait: 'No body content' and lists returned fields, but lacks detail on pagination, sorting, auth requirements, or limits. No annotations provided to supplement.

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 concise sentences with front-loaded main action and efficient listing of returns; no redundant 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?

Complete enough for a parameterless list tool with explicit field listing, but lacks optional details on ordering or error handling.

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; description adds meaning by detailing what the tool returns (slug, title, URLs, etc.), compensating for absence of output 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 'List all ChangeGamer resources', specifies the verb and resource scope, and distinguishes from siblings like get_resource (singular) and search_resources (filtered).

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?

No explicit guidance on when to use this tool over alternatives; does not state conditions or exclusions.

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 does it well. It discloses server-side ranking with the specific field weights (title, then tags, then description), and clearly states the return payload contains type, slug, title, and .md URL with no body content. This is genuinely useful behavioral detail beyond the input schema and tells the agent what to expect from a call.

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 compact and front-loaded. Every sentence adds value: the first states scope and ranking behavior, the second defines the return format and its limitation, and the third routes the follow-up fetching. It is concise without omitting needed 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 read-only keyword search tool, the description is sufficient. It explains the ranking, output fields, the absence of body content, and the next-step tools to get full content. It also has no output schema, so the explicit return-shape description compensates well. Pagination is fully covered by the limit parameter.

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 covers 100% of the parameters, both with solid descriptions, max/min values, and the default limit. The description does not need to add parameter-level detail, so the baseline 3 applies. It offers no extra parameter meaning beyond what schema provides.

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 ('search') and a precise resource scope ('ALL ChangeGamer content (resources and editorial guides)'), making the tool's function clear. It also distinguishes from the narrower sibling search_resources by emphasizing the 'ALL' content scope and by naming the follow-up fetch 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 gives clear usage context: it is a global keyword search over resources and editorial guides, and it directs the agent to use get_resource/get_article for fetching winners. However, it does not explicitly contrast this tool with the sibling search_resources or state when not to use it, so the guidance is implied rather than fully explicit.

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 full burden. It clearly states that results include metadata and URLs but no body content, which is a key behavioral trait. It does not mention rate limits or auth needs, but the read-only nature is effectively 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?

Two sentences that are front-loaded with the action and details, followed by usage guidance. No wasted 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?

The description covers purpose, output, and usage guidance. With two parameters fully described in schema and no output schema needed, the description is fairly complete. It could mention that results are ranked by relevance, but that is implied by 'ranks resources by relevance'.

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%, so baseline is 3. The description adds value with example keywords for the query parameter, but limit's description is already covered by schema. The examples help an agent form effective queries.

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 the specific verb 'Search' and resource 'ChangeGamer corpus', and details that it ranks by relevance across multiple fields and returns metadata with URLs. This clearly distinguishes it from sibling tools like get_resource (fetches content) and list_resources (likely without keyword 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 instructs to use this tool for searching before fetching with get_resource, providing clear workflow context. It does not give exclusions for when not to use it, but the sibling list implies alternatives.

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