MarketCheck API Docs

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful return-format context (Markdown, resolved tables, curl examples) and the UK path nuance, but does not expose deeper behavioral details like pagination or errors. This is acceptable given the read-only 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?

Two tightly written sentences convey purpose, return format, valid input sources, an example, and the UK-path exception without redundancy. Every clause adds 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?

For a single-parameter, read-only fetch tool with no output schema, the description is complete: it explains what the tool returns, what input to pass, how to obtain that input, and a region-specific variant. Nothing essential for calling 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 documents the single path parameter at 100% coverage. The description adds extra meaning by specifying the path should be a 'Page:' path from search_docs or get_endpoint and gives a concrete example, which helps the agent construct valid input beyond the raw 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 verb and resource ('Fetch one full documentation page as Markdown') and adds distinctive details (resolved parameter tables, curl examples). The input-source note distinguishes it from siblings like search_docs and get_endpoint, so an agent can tell what this tool uniquely 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?

Provides clear context on what input to use: a 'Page:' URL or path returned by search_docs or get_endpoint, with an explicit example and UK-path variant. It does not explicitly state when not to use this tool versus each sibling, but the differentiation is mostly inferable from the description.

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 signal readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral detail: it returns real samples and explicitly notes the sample response is truncated, which is transparent about what the caller should expect.

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 tightly worded sentences carry all the essential information. The purpose and scope are front-loaded, followed directly by a concrete usage example; there is no filler 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?

With no output schema, the description compensates by enumerating what the returned reference contains and noting that the response is truncated. Remaining gaps such as exact formatting or auth requirements are minor for a read-only look-up tool already covered by annotations and 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 coverage is 100%, so both path and method are already fully documented in the input schema. The description adds the practical note to use the path as shown by list_endpoints, but it doesn't need to add further parameter meaning given the schema's completeness.

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 immediately identifies the tool as an 'exact reference for one REST endpoint' and lists its concrete contents: every query parameter, a sample request, and a truncated sample response. It distinguishes itself from list_endpoints by instructing use of paths as shown by that sibling tool.

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 gives clear usage context: use the path returned by list_endpoints, and it provides a concrete example. It doesn't explicitly state when not to use it versus search_docs or get_doc_page, but the reference-vs-docs distinction is reasonably implied.

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 indicate a safe read operation. The description adds useful context beyond that: data comes from a live plan list, is authoritative over docs, and currency is region-dependent (USD for NA, GBP for UK). No contradictions 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?

The description is one compact sentence that front-loads the core subject and packs in content, source, authority, and regional currency details without 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?

For a one-parameter read-only lookup with no output schema, the description adequately covers what the tool returns, where the data comes from, and how region affects the result. Nothing essential is missing for correct 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?

The schema already documents the single region parameter with an enum and default value. The description goes further by explaining the practical effect of the region choice on currency, adding meaning beyond the schema alone.

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 resource ('MarketCheck rate plans') and enumerates what it returns: per-call pricing, monthly quotas, and rate limits. It also distinguishes itself from the documentation sibling tools by stating it is sourced from the live plan 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?

It clearly positions this tool as the authoritative live source over docs, so an agent knows to prefer it over get_doc_page or search_docs for current rate-plan information. It does not explicitly list alternatives, but the context is sufficient for tool 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context: it returns every documented endpoint across regions, includes docs URLs, and approximates scale with '~73 endpoints'. There is 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?

The description is a single compact sentence that front-loads the action and resource, then packs in output fields, regions, and scale without any filler. Every element 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 read-only listing tool with no required parameters and a fully documented schema, this description is complete. It states the output fields, scope, and approximate number of results, so an agent can select and invoke the tool correctly without missing information.

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 'filter' and 'region' already explained in the input schema. The description does not add parameter-level detail beyond that, so the 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 ('List') and resource ('every documented MarketCheck REST endpoint'), then lists the exact fields returned. This clearly distinguishes it from siblings like get_endpoint or search_docs, which target individual or searched endpoints rather than the full catalog.

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 makes clear this is the enumeration/catalog tool for all documented endpoints, so an agent knows to use it for overviews or discovery. It does not explicitly state when to prefer get_endpoint or search_docs, but the 'every documented' framing conveys the intended scope well enough to avoid obvious misuse.

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 and destructiveHint=false. The description adds useful behavioral context: it searches a large indexed corpus, returns excerpts, and includes source URLs and endpoint references. This goes beyond the annotations without contradicting them, though it does not cover edge behavior like empty results.

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 tight sentences: scope, output shape, and usage priority. There is no filler or repetition of schema details, and the most important information 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?

Given the 100% schema coverage, read-only annotations, and no output schema, this description fully covers what an agent needs: what is searched, what results look like, and when to use it first. No critical operational detail 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%, with meaningful descriptions for query, k, and region including examples, defaults, and ranges. The description adds no additional parameter-level detail, so the baseline 3 applies—the schema does the heavy lifting.

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: 'Semantic search over the complete MarketCheck API documentation...'. It also states the return value ('most relevant excerpts, each with its source page URL and endpoint'), making the tool's function clear and distinct from direct lookup 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 gives explicit when-to-use guidance: 'Use this first for any question about endpoints, parameters, authentication, errors, plans, or terms.' It does not explicitly name alternatives or exclusion cases, but the priority framing strongly routes an agent to this tool before direct lookups.

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