Nanotoll MCP

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

The description adds valuable behavioral context beyond the annotations: conversion can be lossy (e.g., JSON to CSV flattens nested structures), it requires a Bearer API key, billing is per input character, and HTTP 402 is returned on insufficient balance. These are exactly the kind of runtime traits an agent needs and that annotations do not provide.

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 purpose and return value, then covers lossiness, auth, billing, and alternatives. Every clause earns its place and there is no fluff or tautology.

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 three-parameter transform tool with no output schema, the description covers operation, return value, lossy edge cases, auth requirements, billing behavior, and error condition. The only remaining details are in the schema, so the agent has everything needed to invoke 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%, so the schema already documents all three parameters and their enums. The description does not add additional parameter-level meaning, such as format-specific edge cases or guidance on choosing `from_format`, 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 opens with a specific verb and resource: 'Transform text between JSON, YAML, TOML, and CSV formats.' It also names what the tool returns, which clarifies its function. It further distinguishes itself from siblings by explicitly directing comment-removal to `prune` and llms.txt generation to `generate`.

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 exclusions and alternatives: 'For removing comments use `prune`, for generating llms.txt use `generate`.' This tells the agent when not to use `convert` and what to use instead, which is explicit routing guidance beyond just naming the 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?

Despite safe annotations (readOnlyHint, idempotentHint, destructiveHint), the description adds meaningful behavioral context: authentication via Bearer token, billing per combined input character, and the return shape including metrics. This goes well beyond what annotations alone convey.

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, each earning its place: the core operation, the return value, and the auth/billing/alternative guidance. The most important information is front-loaded, with no 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 two-parameter, side-effect-free tool, the description covers inputs, output format, authentication, billing, and the key alternative. Since there is no output schema, disclosing the returned metrics is especially valuable and makes the tool self-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 coverage is 100%, so the schema fully documents both parameters. The description mostly repeats the before/after semantics already present in the schema, adding no new parameter-specific meaning. 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 and resource: 'Generate a unified diff between two text versions.' It clearly identifies the inputs (before/after) and differentiates itself from the sibling `patch` by explicitly stating that applying diffs is a separate operation.

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 says when to use this tool (to generate a diff) and explicitly names `patch` as the alternative when the goal is to apply a diff. It does not address every sibling, but the key alternative is clearly distinguished.

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 already mark this as read-only and idempotent, but the description adds valuable behavior beyond those hints: it specifies the JSON return shape with per-flow breakdown, the HTTP 422 error for unknown models, the public pricing endpoint, Bearer token requirement, and per-input-character billing. This is rich behavioral context that the annotations do not cover.

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 tightly packed sentences that all earn their place: what it does, what it returns, and how errors/auth work. The main purpose is front-loaded, and there is no fluff or repetition.

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 read-only tool with no output schema, the description covers everything an agent needs: return structure, error behavior, authentication, pricing-source lookup, and billing model. It is complete even without an 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 description coverage is 100%, with each parameter documented simply (model, input_tokens, output_tokens). The description adds context about model-name validation against a pricing list and billing per input character, but it doesn't add significant per-parameter meaning beyond 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 'Estimate the USD cost of an LLM API call', a specific verb and resource that clearly distinguishes this tool from its siblings (convert, diff, generate, etc.), none of which perform cost estimation. It further specifies the CWEP four-flow model, removing ambiguity about what is estimated.

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 clear context: this tool is used to estimate cost before an LLM API call, and it explains the prerequisite of a valid API key. It doesn't explicitly state when not to use it or name alternative tools, but no sibling has overlapping functionality, so the guidance is sufficient.

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 already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, and the description adds value beyond them: the return shape ('llms.txt content and output metrics'), the authentication requirement (Bearer token), and the cost model ('billing is per input character'). No contradiction exists — generating derived content from inputs is a non-mutating, idempotent operation consistent with the annotations.

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 waste: sentence one states the core function, sentence two packs return value, auth, and billing, sentence three routes the compliance use case to a sibling. The main purpose 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 tool with 100% schema coverage, rich annotations, and no output schema, the description covers the operational essentials: purpose, return value, auth, cost, and sibling routing. The minor gaps — specifics of 'output metrics' and error conditions — do not prevent a correct first call.

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 each parameter (name, endpoints, links, pricing, tagline, overview) is already documented in the schema. The description's listing of metadata categories adds framing but no new parameter-level meaning, and it omits tagline. The baseline 3 applies when the schema carries the full burden.

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 opening statement specifies a concrete verb and resource: 'Generate an llms.txt agent-discovery file from structured service metadata (name, endpoints, overview, links, pricing).' It names the input categories and the artifact produced, and it distinguishes the tool from sibling `validate` by naming that alternative explicitly. An agent can tell what this does without opening 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?

The description provides clear when-to-use context (create an llms.txt from structured metadata) and one explicit exclusion: 'To check an existing llms.txt for spec compliance, use `validate`.' However, it does not address the other five siblings (convert, diff, estimate, patch, prune), so the agent must infer those boundaries from the tool name alone.

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?

All annotations are false (readOnlyHint, destructiveHint, etc.), so the description carries the full burden of behavioral disclosure. It discloses that the tool requires authentication and incurs billing, and that it transforms input text. It does not explicitly mention error handling on invalid patches, but it covers the essential behavioral aspects beyond what annotations provide. This is adequate though not exhaustive.

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 four concise sentences, front-loaded with the core action first, then the return value, then requirements, then the alternative. Every sentence serves a purpose with no fluff. The structure is ordered logically for an agent to quickly grasp what the tool does and how to use it.

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 tool with no output schema, the description covers all necessary operational details: the action, return value, authentication and billing, and the related sibling tool. It gives the agent enough to call the tool correctly without missing critical information. There is no requirement for more detail 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 description coverage is 100%, with both 'text' and 'patch' already described in the input schema. The tool description adds minimal extra semantics—only the billing note relates to the inputs. Since the schema already provides the meaning, the baseline of 3 is appropriate; the description doesn't significantly enhance parameter understanding.

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 clear verb and resource: 'Apply a unified diff patch to text, producing the transformed output.' It specifies the operation (apply patch) and result (patched text), and explicitly distinguishes from the sibling tool by saying 'To generate a diff, use `diff`.' This makes the tool's purpose unambiguous and differentiates it from all listed 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 provides explicit guidance: it tells the agent when to use this tool (when applying a patch) and when to use an alternative ('To generate a diff, use `diff`'). It also includes operational requirements (valid API key, billing per combined input characters), which are critical for correct invocation and cost-awareness. This is clear and actionable.

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 already declare readOnlyHint, idempotentHint, and destructiveHint, and the description builds on this by adding auth requirements (Bearer token), billing model (per input character), a specific failure mode (HTTP 402), and return contents (pruned text and character metrics). This is useful operational context beyond the structured annotations.

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 core action and return value come first, followed by auth/billing and sibling routing. Each sentence earns its place and the content is front-loaded.

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's explicit mention of return contents (pruned text and character metrics) is essential and present. Auth, billing, error conditions, and parameter behavior are all covered between the description and a fully documented input schema. Nothing critical is missing for a 3-parameter read-only transformer.

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%: payload, comment_prefix, and remove_empty_lines all have detailed descriptions with constraints and defaults. The tool description adds an overall purpose but no per-parameter detail, so the schema carries the semantic load. A baseline 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 states a specific verb-resource pair: 'Strip comments and empty lines from text' and ties it to a concrete goal, reducing token count before LLM use. It also differentiates from adjacent siblings by explicitly routing format conversion to `convert` and llms.txt generation to `generate`.

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 alternatives for two related siblings ('For format conversion use `convert`, for generating llms.txt use `generate`') and implies the tool's purpose for pruning. It does not explicitly discuss every sibling or enumerate when-not-to-use cases, but the main relevant alternatives are covered.

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 already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds valuable behavioral context beyond those: token-budget truncation, DOM-density boilerplate stripping, API key requirements, flat pricing per read, HTTP 402 for insufficient balance, and HTTP 422 with refund for fetch failures. No contradiction with annotations.

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, each earning its place: purpose, processing behavior, auth/billing, and failure handling. The core purpose is front-loaded, and no padding or redundant restatement of the schema exists.

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 straightforward read-oriented tool with no output schema, the description covers what the tool does, how it processes input, what credentials are needed, what it costs, and what errors to expect. Nothing essential is missing for an agent to invoke 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%, so the parameters are already fully documented. The description only adds marginal context around max_tokens via 'strict token budget' and 'estimated tokens' but does not meaningfully enrich the meaning of url, include_links, or preserve_tables beyond what the 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 opens with a specific verb and resource: 'Fetch a web page and return clean GitHub-flavored markdown.' This unambiguously distinguishes the tool from siblings like convert or generate, even without naming them. The title 'Web Page Reader' reinforces the purpose.

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 for when to use this tool: whenever a web page needs to be fetched and converted to markdown. It does not explicitly mention when not to use it or name an alternative, but the use case is communicated clearly enough for an agent to select it appropriately.

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?

Even with annotations covering readOnly/idempotent/non-destructive, the description adds valuable behavioral context: requires a valid API key (Bearer token), billing is per input character, and it returns a validation report with errors, warnings, and structural flags. This goes beyond the annotation metadata.

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 concise sentences front-load the purpose, then quickly cover return behavior, auth/billing, and the sibling pointer. No redundant information 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 one-parameter read-only tool with full schema coverage and annotations, the description covers purpose, return format, auth requirements, billing, and alternative usage. An agent has everything necessary to invoke 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?

The schema already provides 100% coverage for the single 'content' parameter, and the description does not add parameter-specific detail beyond what the schema states. Baseline 3 is appropriate since the schema carries the burden.

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?

Explicitly states the tool validates llms.txt content for spec compliance, enumerating criteria (H1 title, endpoints section, pricing section, structure). It also differentiates itself from the sibling 'generate', making its scope unambiguous.

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 states when to use the tool (validate llms.txt payload) and gives an explicit when-not by recommending 'generate' for creating new llms.txt. This provides a clear boundary against a relevant alternative.

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