livedatalink

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

No annotations are provided, so the description carries full burden. It explicitly states the return values (US AQI, PM2.5, etc.) and health category rating, providing sufficient transparency for a read-only query tool without side effects.

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 sentence that lists the returned data, followed by a list of example queries. It is front-loaded with the tool's purpose and is highly concise with no redundant 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?

Given the tool's simplicity (one parameter, no output schema), the description fully covers what the tool does, what data it returns, and when to use it. No additional information is needed.

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 'location' is described as 'City, zip code, or place name' in the schema. The description adds value by showing example queries that demonstrate valid inputs, going beyond the schema's bare 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 it retrieves current air quality data for any location, listing specific pollutants and health ratings. It is distinct from siblings which focus on topics like colleges, courts, or weather.

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 a list of example queries that indicate when to use this tool (e.g., 'what's the air quality?'). While it does not explicitly state when not to use it or mention alternatives, the examples strongly imply appropriate contexts.

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 states 'Returns a monthly time series' and 'Keyless official BLS data', but does not disclose data range, update frequency, or any limitations. Adequate but minimal.

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 plus a list of indicators. Efficient and front-loaded with purpose. Slightly more verbose due to listing indicators, but avoids unnecessary detail.

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 data retrieval tool with 3 parameters and no output schema, the description covers the return type (monthly time series) and data source. Missing details like data start year, but overall adequate for typical 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 coverage is 100% with clear descriptions for all three parameters. The description's mention of indicators and suggestion of date range adds little 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 clearly states it provides US labor & price statistics from the Bureau of Labor Statistics using friendly names, listing all available indicators and noting it returns monthly time series. This distinguishes it from the sibling bls_series tool which uses series IDs.

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 common BLS indicators by name, but does not explicitly state when to use this versus bls_series or provide exclusions. The context is adequate but not directive.

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 must carry full burden. Only states 'Fetch any BLS time series' without disclosing rate limits, error handling, data freshness, or return format. Lacks critical behavioral 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, 28 words, no redundancy. Front-loaded with the primary action and includes an example. Highly 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?

No output schema; description does not indicate return format (e.g., time series data points). Partially complete for a simple lookup but lacks details on what the response contains. With 3 parameters and no output schema, more context is warranted.

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 all parameters described. Description adds an example series ID and context but does not significantly augment meaning beyond the schema. 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?

Clearly states the tool fetches a BLS time series by its raw series ID, with an example (LNS14000000). Distinguishes itself from potential siblings like bls_indicator by specifying usage when the exact ID is known.

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 usage condition: 'For when you know the exact BLS series ID.' Implies alternative tools for when ID is unknown, but does not explicitly mention siblings or list when-not-to-use scenarios.

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 the fields returned but does not mention error behavior (e.g., when ID not found), rate limits, or whether it is read-only (implied). Basic transparency is present but 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?

Single sentence of 23 words, front-loaded with purpose, then parenthetical list of fields. No filler, highly 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 the tool's simplicity (1 param, no output schema), the description adequately covers what the tool returns. It lists many metadata fields but omits structural details (e.g., types, nesting) and error handling. Still, it is mostly complete for a lookup 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%, so the parameter description in schema already explains 'gutenberg_id'. The tool description repeats 'by its Project Gutenberg id' without adding new 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?

Description clearly states verb 'Get' + resource 'full catalog metadata for a single book by its Project Gutenberg id', listing specific fields (title, authors, etc.). This distinguishes it from sibling tools like book_search (search multiple) and book_get_text (get text).

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?

Implies usage when you have a Gutenberg ID and need metadata. However, no explicit guidance on when to use this over book_search, book_status, or other book-related tools. Alternatives are not mentioned.

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 behavioral disclosure burden. It explains the return format (passages with title, author, snippet) and mentions default/max limit, but lacks details on rate limits, error handling, or behavior for missing queries.

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, efficiently conveying the core functionality and optional restriction. It is front-loaded and every sentence adds value, though a bulleted structure could improve scanability. Still, very concise.

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 moderate complexity, the description adequately explains input (query, limit, optional gutenberg_id) and output (passages with metadata). No output schema exists, but the description compensates by stating the return format. Edge cases are not addressed, but overall completeness is high for a search 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?

The input schema has 100% coverage, but the description adds value by clarifying that query is a phrase or keywords, and that gutenberg_id restricts to a single book. It also explicitly ties parameters to the output (passages with book title, author, snippet), enhancing understanding beyond 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 clearly states that the tool searches inside books for phrases/keywords and returns passages with book title, author, and snippet. It distinguishes itself from sibling tools like book_search (metadata search) and book_get_text (full text) by focusing on internal content search with snippet results.

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 positions this as the 'headline feature' for finding passages across books, but does not explicitly state when to avoid using it or compare with similar sibling tools like paper_fulltext_search or caselaw_opinion_text. Guidance is implied but not comprehensive.

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 discloses key behaviors: pagination mechanism, fallback URL for unindexed books, and the source (Gutenberg). It does not mention error handling or rate limits, but covers the main operational aspects.

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 succinct sentences deliver all necessary information without repetition. Every word serves a purpose.

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 explains the two types of returns (full text or URL). Covers pagination and fallback. Could mention output format but is sufficient for agent 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 coverage is 100% with clear descriptions for each parameter. The description reinforces pagination usage but adds little beyond the schema. 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?

Clearly states the tool returns full text of an indexed book by Gutenberg ID with pagination, and specifies fallback behavior for non-indexed books. Distinguishes from sibling tools by focusing on text retrieval rather than metadata 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?

Provides explicit instructions for pagination using from_seq and max_passages, and explains fallback to a plain-text URL for non-indexed books. While it doesn't explicitly say when not to use, the guidance 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?

With no annotations provided, the description must disclose behavioral traits. It does well by explaining the live nature of the search, ranking by relevance then popularity, and the exact return fields. However, it omits rate limits or authentication requirements, though these are likely not applicable.

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 concise with four focused sentences. It front-loads the core search capability, then adds ranking and return details, and ends with a usage guideline. No unnecessary 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 no output schema, the description lists return fields (Gutenberg id, title, author(s), language, download count) which is helpful. It also mentions catalog size and live data. It could mention the default limit, but the schema covers that.

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 provides detailed descriptions for all 5 parameters (100% coverage). The description adds that ranking is by relevance then popularity, which provides context beyond the schema, but does not significantly enhance parameter 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 that this tool searches the full Project Gutenberg catalog live via Gutendex by keyword, with optional filters. It distinguishes itself from the sibling tool book_fulltext_search by explicitly directing users to that tool for full-text 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 provides guidance on when to use an alternative: 'Use book_fulltext_search to search inside the locally indexed top books.' This helps the agent choose between similar 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?

No annotations exist; description discloses the tool returns catalog source info and corpus counts. However, it doesn't mention performance, freshness, or any side effects. Adequate but not highly 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 a single, well-structured sentence that front-loads the verb and purpose. No unnecessary 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 no parameters and no output schema, the description adequately explains what the tool returns. Missing output format details, but sufficient for agent understanding.

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; baseline is 4. No additional parameter info needed 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 reports the books store status, specifying components (Gutendex catalog and D1 indexed-corpus counts). It distinguishes from sibling tools like book_search or book_details which are for specific book queries.

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?

Usage is implied for status overview, but no explicit when-to-use or alternatives are mentioned. With many sibling tools, more guidance would help.

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 mentions 'Keyless' (no auth needed) and lists output fields, but does not explicitly state that the tool is read-only or non-destructive, nor does it discuss rate limits or data freshness. For a simple lookup, the behavioral disclosure is minimal.

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 purpose and key output fields. Every word is necessary; no redundancy or filler. Ideal 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?

Given the low complexity (1 parameter, no output schema, no nested objects), the description is fairly complete. It lists the expected return fields (version, description, downloads, etc.) which compensates for the missing output schema. Minor gap: no mention of error handling or case sensitivity of the crate name.

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% (the only parameter 'name' is fully described). The description adds no extra meaning beyond the schema—it merely repeats the lookup purpose. Baseline score of 3 is appropriate as the schema already provides adequate parameter documentation.

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 'Look up' and the resource 'Rust crate on crates.io', listing specific fields returned (version, downloads, etc.). It distinguishes itself from sibling package tools like npm_package by explicitly targeting Rust crates.

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 Rust crate lookups but does not provide explicit guidance on when to use this tool versus alternatives (e.g., npm_package, pypi_package). No exclusions or prerequisites are mentioned.

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 must convey behavioral traits. It uses 'Get' which implies a read-only operation, and lists the metadata returned, suggesting no side effects. However, it does not explicitly confirm it is safe, idempotent, or clarify rate limits 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?

The description is a single, well-structured sentence of about 20 words. It is front-loaded and contains no superfluous information, earning 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 the tool's low complexity (one required parameter, no output schema), the description lists the fields returned and the identifier. It does not specify the format (e.g., JSON) or error conditions, but it is largely sufficient for an agent to understand and invoke 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?

The input schema covers the single parameter 'id' with a description and example. The schema description coverage is 100%. The tool description merely reiterates the role of the CAP id and lists return fields, adding minimal meaning 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 action ('Get'), the resource ('full metadata for a single case'), and the identifier ('CAP id'). It distinguishes this tool from siblings like caselaw_search or caselaw_opinion_text by focusing on retrieving metadata for a specific case.

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 when you have a CAP id, but does not explicitly state when not to use it or provide alternatives. It mentions the fields returned, which helps contextualize its use, but lacks direct guidance on 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?

No annotations are provided, so the description carries the full burden. It states it returns case metadata including CAP id but does not disclose error handling, rate limits, or authentication requirements.

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 action and examples, with no redundant information. 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?

For a simple tool with one parameter and no output schema, the description covers the core function and hints at output structure (CAP id). It could mention more about the full return value, but it's sufficient for most use cases.

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 described). The description adds example citations and mentions parallel citations, but this adds limited value beyond the schema's 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 resolves a reporter citation to its case, with examples of citations. It distinguishes from sibling tools like caselaw_search and caselaw_opinion_text by focusing on citation resolution.

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 have a citation) but does not explicitly state when not to use it or provide alternatives. It lacks explicit guidance on trade-offs.

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 that text is retrieved live from a static mirror (not stored) and includes each opinion with author. But it lacks details on error handling, rate limits, or response format, which are important for a read tool with no output schema.

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 concise sentences, front-loaded with the core purpose, followed by behavioral and parameter guidance. No extraneous information; 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 tool has no output schema and no annotations. The description covers source, content, and max_chars, but does not describe the response format (e.g., plain text vs JSON) or handle edge cases. For a simple retrieval tool, it is adequate but not 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 schema coverage is 100%, so the baseline is 3. The description adds minimal value beyond the schema, only reiterating the use of max_chars to bound response. No new semantic info is provided.

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 fetches opinion text for a given CAP id, distinguishes from siblings like caselaw_case_details (metadata) and caselaw_search (finding IDs). It is specific and aligned with 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?

The description implies usage when you have a CAP id from caselaw_search or caselaw_citation_lookup and mentions using max_chars to bound response. However, it does not explicitly state when not to use or contrast with siblings, leaving some room for ambiguity.

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 scope limitation (v1 index covers U.S. Reports) but does not cover rate limits, authentication, or details about the return structure beyond 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?

Two sentences plus a short note, front-loaded with purpose and followed by output and usage guidance. No filler or redundant 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?

Given 6 parameters, no required ones, and no output schema, the description adequately explains what it returns (metadata with IDs and citations) and the index scope. It is complete for a search 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%, so baseline is 3. The description adds concrete examples (e.g., 'Brown Board Education', 'Supreme Court') and clarifies ISO date format, going beyond the 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 clearly states the tool searches US court opinions by case name/keyword, court, jurisdiction, and date range, returning metadata with IDs and citations. It distinguishes itself from the sibling caselaw_opinion_text by indicating it returns IDs for full-text retrieval.

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 advises using caselaw_opinion_text with returned IDs, but does not explicitly contrast with other court search siblings or state when not to use this tool. The scope note (v1 index covers U.S. Reports) provides useful contextual 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?

No annotations are provided, so the description must disclose behavioral traits. It accurately describes the output (business data) and the optional filter, but does not explicitly state that it is a read-only query or mention any authentication or rate limits. The description is generally transparent but lacks explicit safety cues.

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 extremely concise (two sentences) and front-loaded with the core purpose. Every sentence adds value: the first defines the data and source, the second gives use cases. No redundant or vague phrasing.

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 9 parameters (some conditional) and no output schema or annotations, the description covers the essential data content and use cases but omits behavioral details (e.g., that it is a query-only tool) and does not explain the relationship between parameters (e.g., required fields for different levels). Adequate but could be more 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 100%, so baseline is 3. The description adds context about the NAICS filter and summarises the data contents, but adds little beyond what the schema descriptions already provide for individual parameters. No parameter-specific enhancements.

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's data source (County Business Patterns) and its contents (business establishments, employment, annual payroll), and lists specific use cases (industry research, competitive intel, supply chain analysis). This distinguishes it from sibling census tools like census_demographics or census_population.

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 business-related research by mentioning optional NAICS filter and use cases, but does not explicitly guide when to choose this tool over other census tools (e.g., demographics, population). It would benefit from a sentence like 'Use for business statistics; for demographics use census_demographics.'

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 must carry the full burden. It describes the data retrieved but does not disclose behavioral traits such as pagination, response format, or any side effects (e.g., rate limits). For a read-heavy tool, additional transparency on limits or output structure would be beneficial.

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, front-loading the key functionality and use cases. No unnecessary words 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?

The description covers the core purpose and use cases but lacks detail on return value structure or parameter combinations. Given the tool has 8 parameters and no output schema, more context on expected results would improve completeness.

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 adds no new parameter-level details beyond listing the broad data categories, which does not significantly enhance 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 provides labor force, unemployment, commute times, and other related metrics for US geographies, with specific use cases (site selection, workforce analysis). This sufficiently distinguishes it from sibling census tools like census_demographics or census_income_housing.

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 workforce and real estate analysis but does not provide explicit guidance on when to use this tool versus alternatives or exclude other tools. No prerequisites or when-not-to-use information is given.

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 accurately describes the tool as returning demographic counts and median age, implying a read-only operation. However, it does not disclose potential rate limits, authentication needs, or behavior for missing data. Nonetheless, the description is consistent and factual.

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 extremely concise (two sentences) with no wasted words. It front-loads the key purpose and follows with supporting 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?

Given no output schema, the description should clarify the return format. It lists the fields but does not specify whether the result is a single object or array, nor does it mention default values (e.g., year defaults to 2023). The complexity is moderate, but the description leaves some ambiguity about output structure.

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 parameters are already well-documented. The description adds context about the data source (ACS 5-year) and the returned fields, but does not enhance parameter meaning beyond what the schema provides. 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 clearly states the tool returns race, ethnicity, and age breakdown for a US geography, listing specific categories (white, black, Asian, etc.) and median age. It distinguishes from sibling tools like census_population which likely returns total counts.

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 the data source and scope (US geography) but does not provide explicit guidance on when to use this tool versus alternatives (e.g., census_population for total counts, census_income_housing for economic data). No exclusions or prerequisites are mentioned.

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 mentions lookup behavior and supported query types but does not disclose rate limits, read-only nature, or other traits. Adequate but 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?

Two sentences, front-loaded with action and resource. No wasted words. Perfectly concise.

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 lookup tool without output schema, the description explains what it returns (FIPS codes) and why it's needed (for other census tools). Complete 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%, but the description adds value by explaining how query works: 'state name or 2-letter code, ZIP code (5 digits), and (for state) substring matching.' This enriches the 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 clearly states the tool's purpose: 'Look up Census FIPS codes by name.' It specifies supported query types (state name, code, ZIP, substring) and distinguishes from sibling census 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 explicitly says 'Use this to find the FIPS codes needed by other census_* tools,' indicating when to use it. It doesn't specify 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?

No annotations provided. The description implies a read operation but does not disclose behavioral traits such as idempotency, rate limits, or data freshness. Lacks explicit read-only confirmation.

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 returned data fields, second states use cases. No redundancy, well 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?

With no output schema, the description lists the types of data returned. Lacks details on output format or pagination, but sufficient for a data retrieval tool with 8 parameters.

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 clear parameter descriptions. The tool description adds overall context but does not enhance individual parameter semantics 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 clearly lists specific data points (median income, housing units, etc.) and states use cases for real estate AI and market analysis. However, it does not explicitly differentiate from sibling tools like census_demographics, though the data types are distinct.

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?

Implies usage from context ('real estate AI, market analysis, location-based pricing') but no explicit when-to-use or when-not-to-use guidance compared to 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 must convey behavioral traits. It discloses that data comes from ACS 5-year estimates (via the 'year' parameter) and lists output fields. However, it does not mention rate limits, authentication needs, error handling (e.g., missing geography), or whether the operation is read-only.

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 and output. Every sentence adds value: first defines functionality, second suggests use cases. No unnecessary words 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?

Given the tool's complexity (8 parameters, multiple geography levels) and full schema coverage, the description adequately covers the main output and use cases. However, it does not explain required parameter combinations or the response format, which would be more helpful.

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 does not add meaning beyond the schema's parameter descriptions; it only restates geography levels and output fields. It does not clarify dependencies or format for parameters like 'tract' or 'msa'.

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 retrieves total population for US geographies, lists specific levels (state, county, ZIP, etc.), and specifies returned fields (total, male, female, median age). It also mentions use cases, distinguishing it from sibling tools like census_demographics which likely provide more detailed demographic data.

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 context for usage ('market sizing, location intelligence, demographic analysis') but does not explicitly compare to sibling tools or state when not to use this tool. No exclusions or alternatives are mentioned, leaving the agent to infer optimal usage.

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 must carry the burden. It mentions data source dependency (DAPIP) which adds transparency, but omits details like read-only nature, authentication, or rate limits.

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, front-loaded sentence with no unnecessary words. It efficiently conveys the tool's output.

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 single parameter and no output schema, the description adequately describes the returned data. It mentions conditional availability (DAPIP publication) which adds context. Could be improved by noting the lookup is for a single institution.

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 clear description for unit_id. The description does not add extra parameter meaning beyond what the schema provides, so baseline score 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 clearly states the tool returns current institutional accreditation status, accreditor, last action date, and programmatic accreditations. It distinguishes from sibling tools like college_demographics or college_metrics which cover other aspects.

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. The description does not mention scenarios where other college tools would be more appropriate.

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 must fully disclose behavioral traits. It only mentions the input parameter and general comparison categories. It fails to state that the tool is read-only, what the output format is (e.g., structured data), or any constraints like rate limits or authentication.

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 extremely concise: one sentence explaining purpose plus a short instruction. It is front-loaded with the core action. Every word is necessary with no fluff.

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 or output schema, the description is incomplete. It explains what the tool does and the required input but does not describe the return format, pagination, or any additional context about how the comparison is presented. A more complete description would briefly outline the output structure.

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 covers the single parameter with a description of 'Array of 2-5 IPEDS UNITIDs.' The description adds 'Pass UNITIDs,' which is redundant. Since schema coverage is 100%, the description adds minimal value beyond restating the parameter's purpose.

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 performs a side-by-side comparison of 2-5 schools across cost, outcomes, and admissions metrics. The verb 'compare' and resource 'schools' are specific. It distinguishes from sibling tools like college_metrics (single school) and college_trends (time series).

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 implicitly indicates when to use: when a comparative analysis of multiple schools is needed. It does not explicitly exclude alternatives or specify when not to use, but the context of 'comparison' against siblings like college_search or college_metrics is clear enough for 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?

No annotations provided, so description carries full burden. It states the tool returns demographics but does not disclose behavioral traits such as read-only nature, data freshness, or any side effects. For a single-school lookup, likely safe, but not explicitly 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?

Single sentence with no unnecessary words. Immediately conveys the tool's purpose and outputs. Highly 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 the low complexity and sole parameter, the description adequately covers what the tool does. However, without output schema, it could include a note on return format or that data is for a single school. Mostly 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 100% for the single required parameter (unit_id). Description adds no additional meaning beyond the schema's 'IPEDS UNITID.' 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?

Clearly states the tool returns student-body demographics for one school, listing specific categories (race/ethnicity, gender, age, geographic origin). Distinguishes from siblings like college_search or college_compare which serve different purposes.

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?

Implied usage for obtaining detailed demographics of a single school, but no explicit guidance on when to use this tool versus siblings like college_metrics or college_outcomes_by_program. No exclusion criteria or alternatives mentioned.

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 fails to disclose behavioral traits such as data source, update frequency, or limitations (e.g., only public institutions). It assumes read-only context but does not state it.

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 verb 'cost and outcome metrics', minimal waste.

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 lists the metrics returned. However, it does not specify time frame, data vintage, or restrictions (e.g., only institutions in IPEDS), leaving some 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?

The schema already describes the single parameter (unit_id) as IPEDS UNITID. The description adds no further meaning, but schema coverage is 100%, so 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 clearly enumerates specific metrics (tuition, net price, graduation rate, etc.) for a single school, distinguishing it from sibling tools like college_search and college_compare.

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 one school with a unit_id, but does not provide explicit when/when-not guidance or mention alternatives like college_compare for multiple schools.

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 discloses what data is returned but omits behavioral traits like read-only nature, rate limits, error conditions, or data freshness. The description is minimal for a tool that likely returns multiple records per school.

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 of 21 words, front-loaded with key information. No unnecessary words or repetition. 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?

Given the tool's simplicity (one param, no output schema), the description is fairly complete. It specifies the data types returned. However, it could mention that the output likely contains multiple programs and that the data pertains to a specific year or CIP code range.

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 single parameter (unit_id). The tool description adds context about the returned data but not about the parameter itself. Baseline 3 is appropriate as schema already documents the parameter adequately.

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 provides program-level outcomes for one school, specifying median earnings, median debt, and award counts. It uses a verb (implied 'get') and resource (program-level outcomes), distinguishing from sibling tools like college_demographics or college_metrics.

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 it is for program-level outcomes per school, but does not explicitly state when to use it versus siblings or provide exclusions. Context signals and sibling names offer implicit distinction, but no direct 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?

No annotations are present, and the description does not disclose any behavioral traits such as data source, freshness, rate limits, or behavior on empty results. For a search tool, this lack of transparency leaves agents uncertain about reliability.

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 well-structured sentences with no wasteful words. It front-loads the main action and then lists filters and return fields efficiently.

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 six optional parameters and lack of output schema, the description provides a solid overview of functionality and return data. It could mention pagination or default behavior, but it is largely sufficient for an agent to understand the tool's purpose.

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 baseline is 3. The description adds value by listing the return fields (location, control, etc.) not in the schema, but does not elaborate on parameter semantics beyond what the schema already 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 clearly states it searches US colleges and universities by various filters and lists the return fields. It effectively communicates the core function, though it does not explicitly differentiate from sibling tools like college_compare or college_demographics.

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 is provided on when to use this tool instead of siblings. With many related college tools on the same server, explicit context about when to choose college_search over alternatives would be very helpful.

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 mentions the data source (Urban Institute Education Data Portal) but fails to disclose behavioral traits such as read-only nature, rate limits, data freshness, or whether the tool returns aggregated or per-year data. This is a significant gap for a data 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?

The description is a single, information-dense sentence that front-loads the purpose and essential choices (metric and year range). No unnecessary 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?

The description adequately explains what the tool does but is incomplete regarding the output format. With no output schema, the agent needs to infer that the result is a time-series of the chosen metric. A brief mention of the output structure would improve completeness.

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 clear descriptions for each parameter. The description adds context by grouping metric choices and implying a year range, but does not provide new information beyond the schema. 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 clearly states 'Multi-year trend for one school', specifying the source (IPEDS), the metrics (enrollment, graduation_rate, retention, cost), and the year range. This effectively differentiates it from siblings like college_metrics (single-year data) and college_compare (comparison across schools).

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 trend data for a single school over time, but does not explicitly state when to use this tool versus alternatives (e.g., college_metrics for single-year data, college_compare for cross-school comparisons). No exclusions or prerequisites are mentioned.

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 lists the return fields but does not disclose error handling (e.g., invalid symbol), data freshness, or authentication requirements. The description is adequate but lacks depth for a tool with no 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 two sentences: the first concisely states purpose and outputs, the second gives usage examples. Every sentence earns its place 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?

Despite lacking an output schema, the description enumerates many return fields (sector, revenue, EPS, etc.) and provides example queries, making it complete for a fundamentals tool. Minor gaps include data scope (e.g., only US stocks?) and freshness, but overall 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%, with the single parameter 'symbol' having a clear description ('Stock ticker symbol (e.g., "AAPL")'). The tool description adds no further semantics beyond the schema, so 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 clearly states what the tool does: 'Get company profile and financial fundamentals.' It lists specific financial metrics and provides example queries. It distinguishes itself from siblings like stock_quote (which focuses on price) by emphasizing comprehensive fundamentals.

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 multiple example queries ('tell me about Apple', 'what does this company do?', etc.) that clearly indicate when to use this tool. However, it does not explicitly exclude cases where a simpler sibling (e.g., stock_quote for price or stock_history for historical data) might be more appropriate.

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 bears full responsibility for behavioral disclosure. It implies a read-only search operation but does not explicitly state safety traits (e.g., no side effects, no destructive actions). No rate limits or authentication needs are 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?

The description consists of two concise sentences: the first explains the purpose and filters, the second describes the output. No unnecessary 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?

Without an output schema, the description should detail the return format more thoroughly. It only mentions 'case summaries with citations,' missing pagination, fields, or result count. The description is adequate but not fully complete for a tool with 7 optional parameters.

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 each parameter has its own description in the schema. The tool description merely lists the search dimensions without adding additional meaning or clarification beyond the schema, so it provides no extra value.

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 searches federal and state court opinions by multiple filters and returns summaries with citations. However, it does not differentiate itself from the sibling tool 'court_opinion_search', which may cause confusion.

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 is provided on when to use this tool versus the similar sibling 'court_opinion_search' or any other alternatives. There is no mention of prerequisites or context for use.

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?

The description provides no behavioral details such as error handling, expected output structure, or whether the operation is read-only. Without annotations, this is a significant gap for a single-purpose 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 a single sentence, concise and front-loaded. However, it could earn its place by adding more useful context, such as expected output format, without becoming 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 the low complexity (1 required parameter) and no output schema, the description should provide what the tool returns (e.g., case details) but does not. This leaves the agent uninformed about the result.

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 description for the citation parameter. The tool description repeats the example but adds no additional meaning beyond the schema, 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 clearly states the tool resolves a legal citation to a CourtListener case record, with an example. It differentiates from sibling tools like court_case_search (which searches by other criteria) by specifying the input is a citation string.

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 is provided on when to use this tool versus alternatives like court_case_search or court_opinion_search. The description implies usage when a citation string is available, but lacks explicit direction 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 bears the full burden of behavioral disclosure. It states the tool returns party list and recent entries from PACER/RECAP, which adds useful context about data source and output nature. However, it does not mention side effects, error handling, or any constraints (e.g., rate limits, data recency), so transparency is adequate but incomplete.

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 long, each adding essential information: what the tool does and what it returns. No redundant or filler content. It is efficiently structured and easy to parse.

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 (two parameters, no output schema, no annotations), the description covers the basic functionality and output. However, it lacks contextual details such as when to prefer this tool over siblings, potential error scenarios, or scope limitations (e.g., only federal dockets). This makes it minimally viable but not fully comprehensive for an AI agent.

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%, meaning both parameters are already described in the input schema. The description provides examples (e.g., 'nysd' and '1:23-cv-04567') that add marginal clarity but do not significantly extend beyond the schema's own descriptions. 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 clearly states the tool looks up a federal docket by court ID and docket number and specifies it returns party list and recent entries from PACER/RECAP. However, it does not explicitly differentiate from sibling tools like court_case_search or court_opinion_search, which may have overlapping functionality.

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 is provided on when to use this tool versus alternatives such as court_case_search or court_opinion_search. There is no mention of prerequisites, limitations, or exclusions, leaving the agent without context for appropriate invocation.

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 states what the tool returns (positions, education, bench history) but does not mention whether it is read-only, if it requires authentication, any rate limits, or how it handles partial name matches. This is minimal transparency for a 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?

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and output. Every word adds value, and there is no redundancy or fluff.

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 (1 parameter, no nested objects, no output schema), the description adequately covers what the tool does and what it returns. It is nearly complete, though it might benefit from noting that partial name searches are supported, which the schema already implies.

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 has 100% coverage for the single parameter 'name', which is well-described in the schema itself. The description adds no additional semantic information beyond what the schema already provides, so a 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 clearly states the verb 'look up' and the resource 'judge profile', specifying that it can be done by name or CourtListener person ID. It distinguishes from sibling tools by focusing specifically on judge profiles, unlike other court-related tools that handle cases, dockets, opinions, etc.

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 implicitly indicates use when seeking a judge's profile, but it provides no explicit guidance on when not to use this tool or mentions alternatives among the many sibling tools. No exclusions or context for optimal use are given.

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 only mentions outputs but does not disclose behavioral aspects like search behavior (e.g., fuzzy matching), pagination, authentication requirements, or rate limits.

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?

A single, well-front-loaded sentence conveys the purpose and key outputs with zero waste. Every word serves a purpose.

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 a simple 3-parameter tool with no output schema or annotations, the description adequately states purpose and outputs but lacks detail on result ordering, pagination, and expected behavior for edge cases.

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 has 100% description coverage for all parameters. The description adds no additional meaning beyond the schema, so 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 clearly states it performs full-text search of court opinions and lists specific outputs (snippets, authors, citations), distinguishing it from sibling tools like court_case_search which likely search case metadata.

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 (e.g., court_case_search, court_docket_lookup). The description does not provide any context 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?

Despite no annotations, the description clearly states the output (audio URLs and transcript snippets), which is sufficient for a straightforward search tool with no side effects. 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 sentence that efficiently conveys the tool's purpose and output, with no unnecessary 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?

The description explains returns (audio URLs and transcript snippets) and covers the search functionality. It lacks output structure details, but given no output schema, it is reasonably complete for a simple search 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?

With 100% schema coverage, the baseline is 3. The description adds context about the court parameter implying federal appellate, but does not provide additional meaning beyond 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 explicitly states the tool searches for SCOTUS and federal appellate oral argument audio, clearly distinguishing it from sibling tools like court_opinion_search. The verb 'Search' and resource are specific.

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 that the tool is for oral argument audio, implying its use case. However, it does not explicitly mention when not to use it or direct to alternatives among the many court-related 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 reveals only that results are ordered newest first. It does not disclose whether authentication or payment (e.g., PACER) is needed, rate limits, or the structure of returned data. Critical behavioral traits are missing.

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, no filler, front-loaded with key action and scope. Every word earns its place; it is concise without being under-specified.

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?

Without an output schema, the description should explain what a docket entry contains (e.g., case number, date, party names). It does not. Also, the 'court' parameter uses abbreviations like 'nysd' without explanation. Given the complexity of legal data, the description is insufficient for an agent to fully understand what the tool returns.

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 'court' and 'limit' are documented in the input schema. The description adds no additional meaning to the parameters beyond the ordering hint. According to guidelines, baseline is 3; the slight extra context (ordering) does not elevate the score significantly.

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 recent docket entries for a specific court, ordered newest first. It uses a specific verb ('recent docket entries filed') and resource ('in a specific court'), and among siblings like court_docket_lookup (for a specific docket) and court_case_search (for case lookup by criteria), it uniquely identifies its function as browsing recent activity.

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 does not explicitly state when to use this tool versus alternatives. Sibling names imply different purposes, but the description provides no direct guidance on when to choose this over court_docket_lookup, court_opinion_search, etc. The usage is implied but not clarified.

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. The description describes the output (comparison table with specific fields) but does not mention read-only nature, error handling, or rate limits. Adequate for a simple read tool but could add more 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?

Two sentences plus example queries. Front-loaded with action and output; every sentence adds value. No unnecessary 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 fully covers purpose, input format, output contents, and example queries. No gaps for an AI agent to select or invoke 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?

Schema description covers the single parameter ('coins') with format 'Comma-separated coin names or tickers, 2-5 coins'. The tool description reinforces this without adding new details. Schema coverage is 100%, 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 clearly states the tool compares 2-5 cryptocurrencies and lists the specific metrics shown (price, 24h change, market cap, volume, rank). It provides example queries differentiating it from single-coin tools like crypto_price or crypto_info.

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 'Use this for... any multi-coin comparison question,' implying when to use. It does not explicitly say when not to use, but the examples and phrase 'multi-coin' sufficiently guide the 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?

No annotations are provided, so the description carries the full burden. It only lists what data is returned but does not disclose any behavioral traits such as whether the operation is read-only, any side effects, required permissions, rate limits, or response size. The read-only nature is inferred but not 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?

The description is one concise sentence followed by a list of example queries, which is efficient and front-loaded. The examples are helpful and not excessive. Minor room for improvement: could be even shorter if examples were omitted, but they add 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?

Despite no output schema, the description lists all major fields returned (description, market data, supply info, all-time high/low, genesis date, blockchain, categories, website links), making the return value clear. For a simple tool with one parameter, this is complete and 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% for the single parameter 'coin', and the schema description already provides clear semantics ('Cryptocurrency name or ticker'). The function description adds no extra information about the parameter, so 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 clearly states the tool gets a detailed profile for any cryptocurrency, listing many fields (description, market data, supply info, etc.). It distinguishes from sibling tools like crypto_price, crypto_compare, and crypto_trending by emphasizing 'profile' and 'background 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?

The description provides explicit usage examples ('tell me about bitcoin', 'what is ethereum?', etc.) that make it clear when to use this tool. However, it lacks explicit when-not-to-use guidance or direct comparison to alternatives, though the examples implicitly cover the scope.

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; the description explains what data is returned (price, change, market cap, etc.) but does not discuss limitations, rate limits, or data freshness. Moderate transparency for a read-only 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 a single paragraph that effectively communicates purpose and usage, but it is verbose with a long list of example queries. Front-loaded with the key action but could be more concise.

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 simple input schema and no output schema, the description covers what the tool does and what it returns. It mentions return fields and supported cryptocurrencies, providing adequate context for selecting and using 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?

The single parameter 'coin' is described in the schema as name or ticker, and the description adds value by listing examples and stating support for thousands of coins via CoinGecko ID, enriching the basic 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 gets price and market data for any cryptocurrency, listing specific data points (price in USD, 24h change, etc.) and numerous example queries, distinguishing it from siblings like crypto_compare and crypto_trending.

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 extensive example queries (e.g., 'what's the price of bitcoin?') that indicate when to use the tool. It does not explicitly exclude alternative tools, but the examples make usage context 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?

No annotations provided, but the description explains it returns trending coins based on search activity. It doesn't mention any side effects or limitations like data latency, but for a simple read-only tool this is adequate.

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 slightly verbose with many example queries, but it's well-structured and front-loaded with the core purpose. Each example sentence serves to clarify usage.

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 is complete: it identifies the data source, the criterion (search activity), and provides usage examples. No missing information 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?

Input schema has zero parameters, so description compensates fully by explaining what the output contains. Schema coverage is 100% trivially.

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 returns top trending coins on CoinGecko based on search activity. It differentiates from sibling tools like crypto_price or crypto_compare by focusing on trending interest rather than prices or comparisons.

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 example queries (e.g., 'what's trending in crypto?') but does not state when not to use this tool or mention alternatives. However, the usage is clearly implied for trending-related questions.

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 return fields (CVSS scores, weakness IDs, references, affected products) and source (NVD). However, does not mention error handling or rate limits, which is 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 efficiently cover purpose, ID format, and return content. No extraneous 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 lookup tool with one parameter and no output schema, the description adequately explains purpose, input format, and returned data. Sibling tools are relevant but don't require explicit comparison.

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 described as 'CVE identifier, e.g. CVE-2024-3094.' Description reinforces the ID format but adds minimal new 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?

Description clearly states it provides full detail for a single CVE by ID, specifies the ID format (CVE-YYYY-NNNN), and lists the returned data (CVSS scores, weakness IDs, references, affected products). This distinguishes it from sibling tools like cve_recent and cve_search_by_keyword.

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?

Implicitly guides usage by specifying 'single CVE by ID', but does not explicitly state when to use this tool versus alternatives like cve_search_by_keyword or cve_recent. No when-not or alternative tool 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 are provided, so the description must disclose behavioral traits. It only mentions parameter defaults and filters but does not specify output format, pagination, rate limits, or whether the tool is read-only. This is insufficient for an agent to understand the tool's 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 a single sentence that is front-loaded with the core purpose. Every word is functional, and no superfluous information is included.

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 and no description of the return format, the agent lacks information about what the tool returns (e.g., fields in each CVE entry). The description only covers input parameters, leaving a significant gap for a tool that outputs a list of CVEs.

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 already describes each parameter. The description adds the default and max value for 'days' and lists the severity values. This provides some additional context but does not significantly enhance 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 retrieves recent CVEs with a configurable lookback window (default 7, max 120 days) and optional vendor/severity filters. This distinguishes it from sibling tools like cve_lookup (specific CVE) and cve_search_by_keyword.

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 retrieving recent CVEs with optional filters, but does not explicitly state when to use this over alternatives like cve_search_by_vendor or cve_search_by_keyword. No exclusions or when-not guidance is provided.

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 only states that the search matches keyword against description text with optional date range. No behavioral traits like rate limits, data freshness, or authentication needs 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?

Two concise sentences that front-load the core purpose. No redundancy or unnecessary 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?

The description covers the basic functionality but lacks information about output format, error handling, or result structure. Since there is no output schema, more context about what is returned would improve completeness.

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 merely summarizes parameter purposes without adding significant new meaning beyond the schema definitions.

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 'search', the resource 'CVE', and the scope 'free-text against CVE description text'. It effectively distinguishes from sibling tools like cve_lookup (by ID) and cve_search_by_vendor (by vendor).

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?

Usage context is implied by the description and sibling names, but no explicit guidance on when to use this vs alternatives is provided. The description does not mention when-not-to-use or suggest other 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?

No annotations are provided, and the description does not disclose behavioral traits such as whether the tool is read-only, rate limits, or output format. Beyond the search action, no transparency is offered.

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 states purpose, second provides a key detail. Front-loaded and no redundant 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?

No output schema and no description of return values or pagination. Useful for a search tool but lacks completeness regarding what the response contains.

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 already documents each parameter. The description adds context about vendor matching against the NVD CPE namespace, which adds value beyond the schema's basic 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?

Description clearly states the action 'Search CVEs' and the resource, with specific filters (vendor, product, date range). Distinguishes itself from siblings like cve_search_by_keyword.

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 versus alternatives like cve_search_by_keyword or cve_lookup. Provides a useful note about vendor namespace matching but lacks when-not or comparative advice.

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 states what the tool returns but does not disclose error behavior for invalid IDs, data freshness, or potential side effects. For a simple lookup, this is acceptable but not thorough.

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, well-structured sentence that front-loads the core action ('MITRE CWE detail by ID') and then lists the returned fields. No filler or unnecessary 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?

Given the tool's simplicity (one parameter, no output schema), the description adequately covers purpose and return fields. However, it lacks details on error handling or the structure of parent/child relationships, which could be important for an agent.

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 cwe_id parameter with an example. The description adds value by clarifying acceptable formats ('CWE-NNN or NNN'), which aids correct invocation. This exceeds the baseline of 3 for high schema 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?

The description clearly states the tool's purpose: retrieving details for a specific CWE ID. It specifies the output fields (name, abstraction, etc.), but does not differentiate from sibling tools like cve_lookup or cwe_recent, which could cause confusion.

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 when a CWE ID is known, but does not explicitly state when to use this tool versus alternatives (e.g., cwe_recent for recent CWEs) or when not to use it. No guidance on prerequisites or error handling.

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 must convey behavioral traits. It lacks details like recency of data, pagination, rate limits, or default ordering. Only mentions output fields, missing crucial context for an agent.

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 efficient sentences with front-loaded purpose and output details. 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?

Covers purpose and output but lacks full behavioral context given six parameters, no output schema, and no annotations. Gaps remain in how limits, ordering, and error handling work.

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 3. Description briefly mentions filters (state, county, incident type, date range) but adds no new semantics beyond existing parameter 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?

Description clearly states it returns recent FEMA disaster declarations with filtering options and specifies output fields. However, it does not differentiate from siblings like disaster_history_summary, which might serve similar purposes.

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?

Usage is implied: use when you need filtered recent disaster declarations. No explicit when-not-to-use or alternative tools mentioned, leaving some ambiguity.

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 mentions bucketing by incident type and year, indicating aggregation, but lacks details on data sources (FEMA), coverage limits (federal only), 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?

Two sentences, zero waste. The first sentence states the action, the second adds target audience and integration context. 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?

With no output schema, the description hints at bucketed results but doesn't specify structure. For a summary tool, more detail on output would be beneficial, but it suffices given high schema coverage.

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 clear descriptions for each parameter. The description adds context about the tool's purpose but does not elaborate on parameter specifics 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 provides a multi-year FEMA disaster summary for a location, bucketing declarations by incident type and year. It distinguishes itself from siblings like 'disaster_declarations' and 'flood_zone_lookup' by targeting cumulative risk assessment for insurance brokers and realtors.

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 identifies target users (insurance brokers, realtors) and implies use with 'property_lookup' address. However, it does not explicitly state when not to use or compare to alternatives like 'disaster_declarations'.

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 read-only nature, data source recency, rate limits, or whether the operation is safe or destructive.

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 sentence, front-loaded with the core purpose, and efficiently lists filtering criteria without extra verbiage.

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 an 8-parameter tool with no output schema and no annotations, the description is too brief, missing explanation of output format, combining filters, ordering, and other behavioral details.

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 high (75%), so the baseline is 3. The description adds some context (e.g., 'region' grouping) but incorrectly mentions 'state name' which is not a 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 clearly states the tool returns recent earthquakes from USGS and lists filtering options (region, magnitude, time), making it distinct from sibling tools which cover other domains.

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 no guidance on when to use this tool versus alternatives, nor does it mention any prerequisites or context for use.

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 the tool returns either a catalog or a time series depending on the 'concept' parameter, and mentions the default taxonomy. It does not cover data freshness or limitations, but the behavior is clearly stated without contradictions.

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 consists of two concise sentences that front-load the main purpose and immediately explain the two usage modes. Every word adds value, 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?

Given three parameters and no output schema, the description adequately explains the return types (catalog or time series) and how parameters affect behavior. It lacks details on pagination, rate limits, or output format, but covers the essential functionality for an AI agent.

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?

Although the input schema has 100% coverage, the description adds significant meaning by explaining the functional impact of the 'concept' parameter (catalog vs. time series) and the default behavior of 'taxonomy'. This goes beyond the schema's 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 uses a specific verb ('Get') and resource ('structured XBRL financial facts for a company'), and clearly distinguishes between two modes (catalog vs. time series) based on the 'concept' parameter. This differentiates it from sibling tools like 'edgar_company_lookup' or 'edgar_filings_by_form_type'.

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 the tool without 'concept' (to get the catalog) and with 'concept' (to get time series). It provides context for usage but does not explicitly mention when not to use it or list alternatives, though the dual-mode explanation serves as 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?

No annotations are provided, so the description carries the full burden. It adds useful behavioral context like ranking exact-ticker first, but does not explicitly state read-only nature or other behaviors.

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 wasted words, front-loaded with purpose. Excellent 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?

Given no output schema and no annotations, the description covers core function, ranking, and prerequisite role. It could list expected output fields but is fairly 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 descriptions for both parameters. The description reinforces their purpose but does not add significant new meaning 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 it looks up a company's CIK by ticker or name, specifies that CIK is required for other EDGAR tools, and mentions ranking by exact-ticker first. This is specific and distinguishes it 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 implies this is a prerequisite for other EDGAR tools, providing clear context. However, it does not explicitly state when not to use it or list 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 full burden. It discloses that the output is stripped of HTML and suitable for LLMs, but fails to mention other behavioral aspects such as rate limits, authentication requirements, or error handling. The mention of 'stripped of HTML' and 'suitable for LLM consumption' adds value, but the coverage is incomplete for a mutation-free fetch 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 consists of two succinct sentences with no wasted words. The first sentence immediately states the purpose and output, making it front-loaded and easy to scan. Every sentence adds unique 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 the absence of an output schema, the description adequately explains the return value (text of primary document, stripped of HTML). It mentions a key sibling tool for workflow. The description is mostly complete for a content-fetching tool, though it omits possible error conditions or the effect of the max_chars parameter. Overall, it covers the essential context well.

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 provides 100% description coverage for all three parameters, so the baseline is 3. The description adds no additional parameter-specific detail beyond mentioning the prerequisite use of edgar_recent_filings, which indirectly supports the accession_number parameter. However, it does not explain format variations or bounds beyond what the schema already states.

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 fetches text content of a specific SEC filing by CIK and accession number, and specifies the output is the primary document stripped of HTML for LLM consumption. It is explicit and distinguishable from siblings like edgar_company_facts or edgar_full_text_search, though it doesn't elaborate on all sibling distinctions.

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 instructs to use edgar_recent_filings first to get the accession number, providing a clear prerequisite and workflow guidance. It is explicit about when to use this tool but does not explicitly mention when not to use it or contrast with alternatives beyond the single sibling.

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 must inform about behavior. It discloses that the tool returns 'accession numbers, filers, and filing dates.' It does not mention authorization, rate limits, or safety, but the read nature is implied. A more explicit 'read-only' statement would improve 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?

Two sentences: first defines core functionality, second adds usage examples and return data. No redundancy, 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 tool with 3 simple parameters and no output schema, the description adequately covers purpose, usage context, and return data. It lacks mention of pagination but the 'limit' parameter is described in 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 each parameter already has a description in the schema. The tool description adds context to the overall purpose but does not add significant meaning to individual parameters beyond the schema. 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 clearly states the tool's action ('Pull... SEC filings'), resource ('filings of a specific form type'), and scope ('across all companies'). This distinguishes it from siblings like 'edgar_company_facts' (company-specific) and 'edgar_recent_filings' (not form-specific).

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 concrete use examples ('all 8-Ks today', 'all S-1s this week'), clearly indicating when to use. However, it does not explicitly mention when not to use or suggest alternative tools, leaving some ambiguity.

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. Describes it as a full-text search using EFTS index with filters, but does not disclose return format, pagination, or any side effects. Adequate but not detailed.

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 filters. No redundant information, 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 no annotations or output schema, the description covers the core function and filters adequately. It could specify output format (e.g., list of filings with snippets) but is complete enough for a search 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%, so parameters are already described. The description adds that it's a 'full-text search' and mentions filtering by form types and date range, but does not add significant meaning beyond what the schema provides. 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?

Clearly states the verb 'search' and resource 'SEC filings via EDGAR EFTS index'. Mentions filtering by form types and date range, distinguishing it from siblings like edgar_filings_by_form_type which likely searches without full-text.

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 context ('useful for finding filings that mention specific terms') but no explicit guidance on when to use vs alternatives like edgar_company_facts or edgar_filings_by_form_type, nor when not to use.

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. It mentions it lists recent filings and returns specific fields, but does not explicitly state it's read-only, lacks time range details, or disclose any side effects. Adequate but minimal.

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: first states purpose, second provides usage guidance. No superfluous words; 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?

The description mentions return fields (accession numbers, filing dates) and sibling tool for details. With no output schema, this is helpful. However, it lacks specifics on recency definition (e.g., default time window), sorting, or error behavior, leaving 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 coverage is 100% with descriptions for both parameters. The description does not add additional meaning beyond what the schema already provides, meeting baseline expectations.

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 recent Form 4 insider transaction filings for a company, specifying the return includes accession numbers and filing dates. It distinguishes from the sibling tool edgar_filing_content by directing users there for detailed data.

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?

Explicit guidance is given to use edgar_filing_content for detailed transaction data, indicating this tool is for high-level overviews. This helps the AI agent decide between 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?

No annotations are provided, so the description carries the full burden. It states it lists filings, implying a read-only operation, but lacks details about pagination, rate limits, data freshness, or default ordering. The behavior is straightforward but minimally documented.

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 cover the main purpose and key filters. No wasted words. The information is front-loaded with the main action 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 simple list tool with 4 parameters and no output schema, the description covers the basics but lacks definition of 'recent', default behavior, and any note on limit parameter. It is adequate but not fully comprehensive.

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 by providing concrete examples of form types (10-K, 10-Q, 8-K, etc.) and clarifying that the identifier can be ticker or CIK, which goes slightly beyond the 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 clearly states it lists recent SEC filings for a company, with filtering by form type and start date. It uses specific verbs and examples. However, it does not explicitly differentiate from the sibling tool 'edgar_filings_by_form_type', which could cause confusion.

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 recent filings with filtering, but it does not provide guidance on when to use this tool versus alternatives like 'edgar_filings_by_form_type' or 'edgar_full_text_search'. No when-not-to-use or prerequisite information.

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. It discloses data cadence (monthly) and defaults but lacks details on rate limits, auth, or data freshness. Schema covers parameters, so description adds minimal extra 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?

Two sentences, front-loaded with purpose, no redundant words. Every sentence adds value: purpose, filter options, defaults.

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 or annotations; description covers purpose and defaults but does not mention return format or pagination. Adequate for a simple data retrieval tool but could be more 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 100%, so baseline is 3. Description adds default values and summarizes parameter roles, which is helpful but not extensive.

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+resource ('Monthly state-level electricity data from EIA') and specifies filter dimensions (state, sector, metric) with defaults. Distinguishes from sibling EIA tools by focusing on state-level electricity metrics.

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 context (when needing state-level electricity data) but does not explicitly exclude alternatives or mention when not to use.

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 data's monthly cadence, sector coverage, and unit, but does not mention pagination, rate limits, or behavior when parameters are omitted. 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 with no fluff, front-loaded with the core purpose and key details. 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?

Given 5 optional parameters and no output schema, the description covers the main purpose and return type, but omits default values for state and sector, and lacks explanation of date parameters beyond schema. Adequate but could be more 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 100%, so all parameters are already documented. The description adds context about return units (BTU equivalents) not in the schema, but overall provides minimal extra meaning beyond what the schema 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?

The description clearly states the tool retrieves monthly US energy consumption by sector from EIA, listing specific sectors and the output unit (BTU equivalents). This distinguishes it from sibling EIA tools that focus on electricity, gasoline prices, or natural gas.

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 versus alternatives like eia_electricity_state or eia_natural_gas. The description implies it's for energy consumption data, but lacks when-not or alternative recommendations.

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 must bear full responsibility for behavioral disclosure. It only describes what the tool does, not any behavioral traits such as data freshness, rate limits, or side effects. The absence of such info 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?

The description is concise with two sentences that convey the core purpose and key parameters. It is front-loaded with the main action and immediately lists filters. Minor improvement could be restructuring for even quicker scanning.

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 an output schema, the description does not elaborate on return format or data structure. It explains the data source and common use cases but lacks completeness for an agent to fully anticipate output. Adequate but not thorough.

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 has 100% description coverage for all 5 parameters, so the baseline is 3. The description adds some context about region codes and grades (e.g., 'PADD1-PADD5 or national') but mostly repeats schema info. It provides marginal added value.

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 weekly US retail gasoline prices from EIA, with filters by region and grade. It distinguishes itself from sibling tools like `eia_natural_gas` or `eia_oil_supply` by specifically targeting gasoline prices.

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 mentions usefulness for fuel-cost analysis, transportation logistics, and consumer price tracking, implying appropriate contexts. However, it does not explicitly state when not to use this tool or suggest alternatives, leaving usage guidance incomplete.

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 exist, so the description carries the full burden. It discloses the data sources (EIA, Henry Hub, NYMEX) and the temporal granularity for each series. No destructive behavior is indicated, and the tool is read-only; the description adequately conveys its 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 extremely concise: two sentences with no wasted words. The main purpose is front-loaded, and the series options are listed clearly. 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?

With 4 parameters, no output schema, and no annotations, the description is adequately complete. It explains the series choices and their meaning. It could optionally mention the output format (tabular), but the given information is sufficient 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 input schema has 100% coverage, so the description need not restate all parameters. It adds value by explaining the semantic meaning of the 'series' enum values (e.g., 'spot' = Henry Hub daily, 'residential' = monthly retail to households) and states the default series, 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 identifies the resource ('US natural gas data from EIA') and the verb (implied retrieval). It lists four named series options with brief explanations ({'spot', 'futures', 'residential', 'storage'}), clearly distinguishing the tool from siblings like eia_electricity_state and eia_gasoline_prices.

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 the four series options and their cadences (daily, monthly, weekly), helping the agent choose correctly. It does not explicitly state when not to use this tool or mention alternatives, but the guidance is clear for selecting the appropriate series.

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 bears full burden for behavioral disclosure. It mentions the tool returns weekly data and lists metrics/regions but omits details like data freshness, API limits, or any potential side effects. The description is minimal beyond parameter listing.

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 sentences, concise, and front-loads the purpose. Every sentence adds relevant information about metrics and filtering. 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?

Given the absence of an output schema, the description adequately covers the data type (weekly crude oil supply), metrics, and region filter. It explains the default metric and region, and the schema documents all parameters. Slight gap: no mention of pagination or return structure, but acceptable for a simple query 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 descriptions for each parameter. The tool description adds default values ('production', 'national') and an example region, providing slight added value. However, it mostly restates schema content, so 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 clearly states the tool retrieves weekly US crude oil supply data and lists four specific metrics. It distinguishes from sibling tools like eia_electricity_state or eia_gasoline_prices by focusing on crude oil supply.

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 implicitly indicates use for crude oil supply data but provides no explicit guidance on when to use this tool versus alternatives (e.g., other EIA tools). No exclusions are stated.

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 bears full responsibility. It discloses the return format (megawatt-hours) and the data source (EIA, monthly). However, it does not specify whether the operation is read-only, potential rate limits, or any caveats about data freshness, which are relevant for a data 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?

The description is extremely concise, using two short sentences to cover the tool's core purpose, sources, state filter, and return format. Every word earns its place with 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?

Given the tool has 5 optional parameters and no output schema or annotations, the description covers the main purpose and key parameters but omits important context: what 'all' includes, how to differentiate from sibling EIA tools, and any pagination behavior despite a limit parameter. It is adequate but has clear 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 coverage is 100%, so baseline is 3. The description adds value by mentioning the unit 'megawatt-hours' and clarifying the default state ('national') and that data is monthly. However, it does not explain what 'all' means as a source, which is a notable gap that the schema's enum also leaves unclear.

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 monthly US electricity generation by source from EIA, listing specific sources. However, the tool name includes 'renewable' while the sources include nuclear (non-renewable) and 'all' (unclear whether it includes fossil fuels). This ambiguity and lack of differentiation from siblings like eia_electricity_state limit clarity.

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 is provided on when to use this tool versus alternatives such as eia_electricity_state or other EIA tools. The description does not mention exclusions or preferred scenarios, leaving the agent without context for selection.

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