Openreview

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

Annotations already ensure safety (readOnlyHint, non-destructive). The description adds critical behavioral context: default model (Workers AI Llama 3.3 70b), cost implications for Anthropic probes, and return format (per-model score, confidence, signals, raw_response). This exceeds what annotations provide.

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

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

Four efficiently crafted sentences, each serving a distinct purpose: main action, model selection and key, output detail, use cases. No redundant text. Front-loaded with the core function.

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 explains return values (per-model and combined view, includes score range 0-100). It covers model options, key usage, and context parameter. All essential aspects are addressed for correct agent invocation.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining entity disambiguation ('helps disambiguate common names'), the pass-through nature of _apiKey, and how the models parameter works (omit for default). This extra context justifies a 4.

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

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

The description clearly states the tool's function: probe LLMs about a topic and score visibility (0-100) per model. It uses specific verbs ('probe', 'score') and nouns ('LLMs', 'visibility'). This distinguishes it from siblings like 'scan_competitor_ai_presence' by focusing on per-model scoring.

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 mentions use cases ('AI-marketing audits, pre-launch brand checks, competitive monitoring') and explains when to use the optional API key. However, it does not directly contrast with similar siblings or provide explicit when-not-to-use guidance, though the context implies its 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, so the description must fully convey behavioral traits. It explains the routing behavior and supported sources but does not disclose error handling, permission requirements, or whether the operation is read-only. This is a moderate gap for a tool that could access sensitive data.

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 and well-structured: first sentence states the core purpose, second gives usage cues, third lists sources, and fourth provides examples. Every sentence adds value without redundancy.

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

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

Given the single parameter and no output schema, the description thoroughly explains the tool's role as a routing layer. It could briefly mention the output format (e.g., raw answer or structured data), but it already says 'returns the result', which is adequate.

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

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

Only one parameter 'question' exists with schema coverage 100%. The description adds significant context by listing example questions and types of queries, enhancing understanding 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 answers natural-language questions by routing to the appropriate data source. It lists multiple domains (SEC EDGAR, FRED, etc.) and provides examples, distinguishing it from sibling tools that are specific operations.

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

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

The description explicitly tells when to use ('Use when a user asks...') and when not to ('you don't want to figure out which Pipeworx pack/tool to call'). It gives concrete examples of appropriate queries, providing clear 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?

Describes internal process: resolves market, classifies bet, fans out to packs, returns evidence packet and comparison. Matches annotations (readOnly, openWorld, non-destructive) and adds detail.

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

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

Four sentences, front-loaded with main action, no redundant information. Efficiently conveys purpose, usage, and behavior.

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 inputs, internal processing, outputs (evidence packet, comparison). Without output schema, description adequately explains return value. Complete for a research 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?

Adds meaning for 'market' (slug, URL, or question text) and 'depth' (quick vs thorough with defaults). Schema coverage is 100% but description enriches understanding.

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

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

The description clearly states the tool researches a Polymarket bet by pulling Pipeworx data. It specifies the verb 'research' and the resource 'Polymarket bet', distinguishing it from sibling tools like ask_pipeworx.

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 example use cases ('should I bet on X?', 'what does the data say?') and implies it's the core tool for bet context. Lacks explicit when-not-to-use but offers good 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 provided, so description carries full burden. Discloses return data (paired data + citation URIs) and source (SEC EDGAR/XBRL, FAERS). Does not mention rate limits, auth needs, or side effects, but as a read operation, it's 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?

Efficient single paragraph with logical flow: core purpose, usage triggers, per-type details, and value proposition. No redundant sentences; every sentence contributes.

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

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

Given no output schema, the description explains return values (paired data + citation URIs) and data sources. With only two simple parameters, this fully equips an agent to invoke correctly.

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

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

Schema coverage is 100% with basic descriptions. The description adds meaningful detail: for type 'company' it lists financial metrics and sources; for 'drug' it lists adverse-event counts, approvals, trials. This goes beyond the enum labels.

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 compares 2-5 companies or drugs side by side, using specific verbs like 'compare' and highlighting use cases such as tables/rankings. It distinguishes from alternatives by noting it replaces 8-15 sequential agent calls.

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

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

Explicitly provides when-to-use triggers like 'compare X and Y' or 'X vs Y', and specifies what each type pulls. Lacks explicit when-not-to-use or alternative tool names, 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 discloses the output format (names + descriptions), default limit (20), and max limit (50). It explains the input parameters adequately. Missing details about edge cases or rate limits, but for a discovery tool, the behavior is sufficiently transparent.

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

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

The description is a single paragraph but is well front-loaded with the main purpose. Every sentence adds value, including a list of domains and a strong usage hint. It is concise enough but could be slightly more structured (e.g., bullet points).

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 covers the return value (names+descriptions) and explains input parameters and usage. It addresses the main use case but lacks discussion of edge cases or error handling. Overall sufficient for the tool's simplicity.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds value by specifying the default and max for 'limit' and emphasizing that 'query' is a natural language description. This enhances 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's purpose: 'Find tools by describing the data or task.' It lists specific domains (SEC filings, financials, etc.) and emphasizes that it returns the top-N most relevant tools. It distinguishes from sibling tools by recommending calling this FIRST to see the option set.

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 when you need to browse, search, look up, or discover what tools exist for...' and 'Call this FIRST...' providing clear context. However, it does not explicitly state when not to use it or list alternative 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 must fully disclose behavior. It details the return data (SEC filings, fundamentals, patents, news, LEI) and citation format. While it could mention call limits or side effects, the core behavioral traits are well explained for a read-only information 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 two sentences: the first states a compelling purpose, and the second provides usage context, return contents, and format. Every sentence serves a purpose, and the most critical 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?

For a tool that aggregates data from multiple sources, the description lists all key data types returned. Without an output schema, this is sufficient for the AI to understand what to expect. Missing details like pagination or error handling are minor given the tool's 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%, so baseline is 3. The description adds value by specifying that 'type' is currently limited to 'company' and that 'value' accepts ticker or CIK but not names, with a clear redirection to 'resolve_entity'. This goes 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 specifies a clear verb+resource ('Get everything about a company in one call') and distinguishes itself from siblings by explicitly noting that it eliminates the need for multiple pack tools and that name resolution requires the sibling 'resolve_entity'.

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 lists use cases ('tell me about X', 'give me a profile', etc.) and provides a direct exclusion: 'Names not supported — use resolve_entity first'. This clearly guides the AI on when and how to invoke the tool.

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 clarifies it's a delete operation. Could mention whether missing keys cause errors, but overall sufficient.

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

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

Two concise, front-loaded sentences with no wasted words.

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

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

Given the simple input (one required string) and no output, the description fully equips the agent to use the tool correctly.

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

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

Schema covers all parameters (100% coverage) with description 'Memory key to delete'. The tool description adds no further semantic value beyond schema.

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

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

Explicitly states the tool deletes a stored memory by key. Distinguishes from siblings (remember, recall) by specifying deletion.

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

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

Provides clear use cases: when context is stale, task done, or clearing sensitive data. Mentions pairing with remember and recall for context.

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

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds that it fetches the page, extracts title/description/key links, and emits standard markdown, going beyond annotations. No contradictions found.

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 (3-4 sentences) and front-loaded with the core purpose. Each sentence adds value: purpose, process, output, use cases. No redundant or filler content.

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 adequately explains output as a text blob in standard format. It covers inputs (url, optional max_links), process, and output. Slightly vague on exact output format but sufficient for the tool's simplicity.

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

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

Input schema has 100% coverage with clear descriptions for both parameters. The description does not add new parameter detail but implies the output includes key links, which indirectly relates to max_links. 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 generates a production-ready llms.txt file for any URL. It specifies the action (generate), resource (llms.txt), and includes extraction steps and output format. No sibling tool directly competes, so it stands 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?

The description lists three concrete use cases (client site indexing, own project drafting, competitor auditing), providing clear context. It does not explicitly state when not to use or name alternatives, but the context is sufficient for typical 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 the description carries full burden. It discloses no behavioral traits such as read-only nature, authentication requirements, rate limits, or error responses. The description is too sparse to inform safe invocation.

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 concise sentence, front-loaded with 'Single note' for quick understanding. No wasted words, though slightly under-specified for full usability.

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?

Minimal description for a simple fetch tool with 2 parameters and no output schema. Lacks details on return format, error handling, or examples. Adequate but not 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?

Input schema covers 100% of parameter descriptions, so the description adds no additional meaning beyond 'notes of various types'. Baselined at 3 as schema does the heavy lifting.

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

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

The description clearly states the tool retrieves a single note, listing possible types (paper, review, comment, decision). It effectively distinguishes from sibling tools like get_paper (specific paper retrieval) and search_notes (listing notes).

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

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

No explicit guidance on when to use this tool versus alternatives. The description does not mention when not to use it or provide context for selection, leaving the agent to infer from the tool name and siblings.

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

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

With no annotations, the description fully discloses that the tool retrieves a paper plus its child notes, a read operation. No mention of auth or rate limits, but for a simple retrieval the behavior is clear.

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

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

Two short sentences convey essential information without any fluff, front-loading the return structure and the required parameter.

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?

Lacks details about return format, error cases (e.g., paper not found), or how it differs from get_note. For a single-parameter tool, more context 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 of forum_id is 100% and description only restates the schema's hint that it is the paper note id, adding no extra semantic value 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?

Description clearly states it returns a paper and all child notes (reviews, rebuttal, decision, metareview), distinguishing it from sibling get_note which likely returns a single note.

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?

Tells user to pass the forum id but does not specify when to use this tool versus alternatives like get_note or search_notes. No explicit when-not or use case guidance.

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

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

With no annotations, the description should disclose behavioral traits. It mentions 'metadata' but does not specify what it includes or any side effects (likely none). Limited 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 very short and to the point, but it is a phrase lacking a complete sentence structure. Still efficient given the tool's simplicity.

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 is provided, and the description does not explain what 'metadata' consists of. Given the single parameter, the description could offer more details about return values or usage context.

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

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

Schema coverage is 100%, and the schema includes an example for group_id. The description adds 'metadata' but not additional semantics 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 retrieves venue metadata by group id, using a specific verb and resource. It distinguishes itself from sibling 'list_venues' which likely returns a list.

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

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

No guidance is provided on when to use this tool versus alternatives like 'list_venues', nor any context about prerequisites 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?

No annotations provided, so description must disclose behavioral traits. It does not mention default sort order, pagination, or error behavior. The description only restates what the input schema already indicates.

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?

Very concise single sentence, but lacks structure such as explicit sections or usage examples. Front-loaded with purpose, but could benefit from more details without adding 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?

Lacks completeness given there is no output schema. Does not describe return format, default pagination, or any constraints. For a tool with 4 parameters and no annotations, the description is insufficient.

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. Description adds no additional parameter semantics beyond noting venue_id is a venue group ID, which is already in the schema.

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

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

Clearly states that the tool lists papers submitted to a venue, and identifies the required venue_id parameter. However, it does not differentiate from sibling tools like search_notes or get_paper, but the intent is clear.

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 no guidance on when to use this tool versus alternatives such as search_notes or get_venue. Only instructs to pass venue_id, which is a parameter requirement, not usage context.

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

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

No annotations; description implies read-only but does not confirm. Lacks mention of pagination or result limits beyond parameter descriptions.

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 front-loads purpose, second adds usage hint. No redundant words.

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

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

Lacks return value details (e.g., fields per venue) but with 3 parameters and simple list, mostly complete. Could mention pagination via offset.

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

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

Schema covers all parameters with descriptions (100% coverage). Description adds no extra meaning beyond what schema provides.

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

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

Clearly states verb 'List', resource 'venue groups', and scope 'conferences, workshops'. Differentiates from sibling 'get_venue' and 'list_submissions' by specifying group-level listing and linking to submissions.

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 pattern: list groups then use id for submissions. No explicit when-not or alternatives, but 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?

With no annotations, the description discloses rate limits (5/identifier/day) and that it doesn't consume tool-call quota. Could mention if any response is returned after submission, but the provided info is sufficient for a feedback 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?

Single paragraph with all key info front-loaded: purpose, usage, limitations. Every sentence serves a 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?

For a simple feedback tool with no output schema, the description covers what, when, and how. Could mention if the user gets an acknowledgment, but otherwise 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% but the description adds context: explains the enum values for 'type' beyond schema descriptions, and gives guidance on writing 'message' (be specific, 1-2 sentences). Adds value without redundancy.

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

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

States clearly that the tool is for telling the Pipeworx team about bugs, missing features, or praise. Explicitly distinguishes from sibling tools by specifying when to use it (wrong data, missing tool, praise).

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

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

Provides explicit when-to-use scenarios (bug, feature/data_gap, praise) and what not to do (don't paste end-user's prompt). Mentions rate limits, free usage, and that the team reads digests daily, which guides the agent on impact.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds behavioral context: data source (CF analytics-engine), no PII, caching (5min-1h), and aggregate output. No contradiction.

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

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

Three well-structured sentences plus bulleted uses. Front-loaded with the main output, then window options, then use cases, then technical 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?

For a simple tool with one optional parameter and no output schema, the description covers input, behavior, use cases, data source, and caching. The return structure (top tools, packs, volume) is described, making it complete.

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

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

The single 'window' parameter has full schema coverage (100%) with an enum description that explains the trade-off between hot vs. steady-state signals. The description adds value 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 returns 'the top tools, top packs, and total call volume' over a recent window, with explicit window options. This distinguishes it from sibling tools like 'discover_tools' or 'bet_research' by focusing on real-time call aggregation.

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?

Three concrete use cases are provided: discovering hot data sources, confirming canonical tools, and seeing alignment with agent needs. There is no explicit when-not-to-use or alternative tool mention, but the context of siblings implies this tool's niche.

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 aligns with annotations (readOnlyHint, openWorldHint, non-destructive) and adds detailed behavioral context: the tool walks child markets, extracts dates/thresholds, sorts, and checks for violations. No 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 is relatively long but well-organized: it explains the concept, the rule, the input, and the output. It could be slightly more concise, but 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?

Despite lacking an output schema, the description fully explains what the tool returns (list of {market_a, market_b, gap_pp, suggested_trade}). It covers the input, process, and output comprehensively, matching the tool's 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?

The single parameter 'event' is well-described in both schema and description. The description adds practical context (e.g., examples, how the tool uses the parameter) beyond the schema's basic type/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's purpose: finding arbitrage opportunities within a Polymarket event by checking monotonicity violations. It explains the underlying logic (e.g., price ordering) and distinguishes itself from other tools like polymarket_edges.

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 explains when to use the tool (for events with multiple date/threshold markets) and how to invoke it (passing a slug or URL). It lacks explicit 'when not to use' guidance but the context is sufficiently 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?

Annotations declare readOnlyHint=true and openWorldHint=true; the description adds rich behavioral detail: uses a lognormal model from FRED + live coinpaprika price, groups by asset, computes model probability, ranks by |edge|, and returns top N with direction. This adds significant value beyond 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 well-structured and front-loaded with purpose, method, output, and use case. Every sentence earns its place without redundancy. It is comprehensive yet 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 no output schema, the description partially specifies the return format (top N with edge magnitude and suggested direction) but could be more explicit about the exact fields per market. The algorithmic explanation compensates, but a slight gap remains.

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 adds context by explaining what 'edge' means (model probability vs market price) and how the ranking works, which enhances understanding of min_edge_pp and limit.

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 scans high-volume Polymarket markets, identifies where Pipeworx data disagrees with market price, and returns top edges with suggested direction. It distinguishes from sibling tools like bet_research and polymarket_arbitrage by focusing on opportunity discovery.

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 frames the tool for the 'what should I bet on today' question and notes it saves agents from manually paging through markets. It implies usage context but does not name specific alternatives or exclusions, which is a minor gap.

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

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

Annotations indicate read-only, idempotent, and non-destructive behavior. The description adds significant context: how the spread is computed (Kalshi - Polymarket in percentage points), output includes leg-by-leg prices in raw probability, and that the delta is a real arb signal. No 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 is moderately long but well-structured, front-loaded with the core concept. Each sentence adds unique information (purpose, motivation, modes, output details). Minor redundancy in explaining two modes, but overall 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?

For a read-only tool with 3 optional parameters and no output schema, the description adequately explains the output format and how to use it. It does not cover error handling or edge cases, but annotations provide safety context.

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

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

Schema covers all 3 parameters with descriptions (100% coverage). The description adds value by listing additional topic options beyond schema and explaining parameter override behavior (explicit overrides topic).

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 computes the cross-venue spread between Kalshi and Polymarket for the same resolving question. It specifies two modes (topic and explicit) and distinguishes from sibling tools like polymarket_arbitrage and polymarket_edges.

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 explains the two usage modes and provides examples (topic shortcuts and explicit tickers). It implies when to use this tool for arb signals but does not explicitly state when not to use it or contrast with 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?

Discloses scoping to the agent's identifier and mentions dual behavior (single key retrieval vs. listing all keys). Slightly lacks explicit statement that it is read-only, but adequate for a 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?

Four concise sentences front-loading the core action and use cases, with zero wasted words. Effectively organized for quick comprehension.

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 (one optional param) and no output schema, the description covers purpose, usage patterns, examples, scoping, and sibling relationships. No obvious 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 a clear description for the sole parameter; the description reinforces the schema without adding significant new semantic detail.

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

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

Clearly states the tool retrieves values saved via 'remember', with an alternate mode to list all keys when argument omitted. Distinguishes from sibling tools 'remember' and 'forget'.

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 concrete use cases (look up target ticker, address, prior notes) and contrasts with 'remember' and 'forget' tools, giving clear context for when to use this tool.

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 explains the tool fans out to SEC EDGAR, GDELT, and USPTO in parallel, and details the return structure (structured changes, count, URIs). This provides sufficient behavioral 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?

The description is a single block but is well-organized: purpose, usage, behavior, parameter details, return format. It is front-loaded with the most important information. Slightly dense but 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?

The tool has 3 parameters all covered, no output schema. The description explains return values (structured changes, count, URIs). It lacks details on pagination or limits but covers the core functionality well given the complexity of parallel fan-out.

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. The description adds value by explaining the 'since' parameter accepts ISO dates or relative shorthand with concrete examples ('7d', '30d', '3m', '1y'), recommends a default ('30d or 1m for typical monitoring'), and clarifies that 'value' can be a ticker or CIK. This goes 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's purpose: finding recent changes for a company. It uses a specific verb ('What's new') and identifies the resource ('company'). The tool is distinct from siblings like entity_profile or search_notes, as it fans out to multiple external sources for recent updates.

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 ('when a user asks...') and common query patterns. It implies the tool is for monitoring changes but does not include when-not-to-use or explicit alternatives, though the clear examples compensate.

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

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

Discloses persistence behavior: scope by identifier, authenticated vs anonymous sessions, 24h retention for anonymous. No annotations provided, so description carries full burden effectively.

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

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

Three concise sentences with front-loaded purpose. No filler, every sentence adds value.

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

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

No output schema, but store operations typically don't need extensive return docs. Could mention overwrite behavior, but overall complete given simplicity.

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

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

Schema already provides 100% coverage with descriptions. Description adds examples of key patterns and notes value is any text, further clarifying usage context.

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

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

Clearly states 'Save data the agent will need to reuse later' and provides concrete examples (ticker, address, preference). Distinguishes from sibling tools recall and forget.

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

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

Explicitly says 'Use when you discover something worth carrying forward' and tells agent to pair with recall and forget.

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 bears full burden. It discloses that it returns IDs plus citation URIs and mentions the ID systems. While it doesn't cover error handling or rate limits, it adequately describes the core behavior for a read-only lookup.

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, front-loaded with the main action, and every sentence provides useful information without redundancy.

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

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

Given no output schema, the description covers return types (IDs and citation URIs) and ID systems. It is largely complete for a simple lookup tool, though it could mention behavior for unrecognized inputs.

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 enriches with concrete examples (e.g., 'Apple → AAPL / CIK 0000320193') and clarifies acceptable value formats, adding value beyond the schema's brief 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: lookup canonical/official identifiers for companies or drugs. It provides specific examples and ID systems (CIK, ticker, RxCUI, LEI), distinguishing it from sibling tools.

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

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

Explicitly tells when to use ('when a user mentions a name and you need the CIK...') and ordering ('Use this BEFORE calling other tools'), and states it replaces multiple lookups, giving clear usage context.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds procedural details: probing each entity with ai_visibility_check, ranking by score, and returning a ranked list with score/confidence/signal density. No contradiction, and the added context is valuable beyond annotations.

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

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

Three sentences, no filler. Front-loaded with the core action, followed by usage context and output description. 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?

Without an output schema, the description adequately explains the return format (ranked list with score, confidence, signal density). It covers the main functionality and use case. Missing explicit mention of prerequisites like API key for anthropic, but the schema handles that. Overall sufficient 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 description coverage is 100%, so the schema already documents all parameters. The description only restates that entities are compared and does not add new semantic detail for individual parameters. 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?

Description clearly states the tool compares AI visibility across multiple entities side-by-side, probes with ai_visibility_check, ranks results, and surfaces differences. It distinguishes from sibling tools like ai_visibility_check (single entity) and compare_entities (general comparison) by specifying the use case for competitive AI-marketing audits.

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 concrete use case and example question ('does Claude know about us as well as our competitors?'), implying when to use. Does not explicitly state when not to use or list alternatives, but the context is clear enough for an AI agent to infer.

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

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

With no annotations, the description must disclose behavioral traits. It only says 'search,' implying read-only, but fails to mention pagination (limit/offset), performance, required permissions, or any side effects. The description 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 short sentence, but it is under-specified and does not convey necessary context. Every sentence should add value; this one is too vague to be useful.

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 parameters, no output schema, and no annotations, the description should be more complete. It does not explain how parameters work together or what the return format looks like, leaving significant 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 input schema has 100% description coverage for all 5 parameters. The description adds no additional meaning beyond the schema, so a baseline of 3 is appropriate.

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

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

The description states 'Full-text search across notes,' which clearly identifies the action (search) and resource (notes). It is specific enough to distinguish from 'get_note' but does not explicitly differentiate from 'ask_pipeworx' or other potential search tools, so a 4 is appropriate.

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 usage guidance is provided. There is no information on when to use this tool over alternatives like 'get_note' or 'ask_pipeworx,' nor any prerequisites or typical 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?

With no annotations, the description fully discloses behavior: it returns a verdict (confirmed, approximately_correct, etc.), actual value with citation, and percent delta. It also mentions underlying data sources and that it replaces multiple sequential calls, giving insight into efficiency and limitations.

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

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

The description is comprehensive but not overly long. It is front-loaded with the main purpose, then includes examples, scope, and return format. Each sentence contributes meaningful information, though minor trimming could improve 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 simple input schema (one parameter, no output schema) and no annotations, the description covers essential aspects: supported claims, verdict types, output details, and efficiency benefits. It lacks information on error handling or rate limits, but is otherwise complete for agent invocation.

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

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

The single 'claim' parameter is described in the schema, and the description adds value by providing concrete examples and specifying the type of claims (e.g., revenue, net income). This enhances understanding beyond the basic 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 defines the tool as fact-checking natural-language claims against authoritative sources, specifying it handles company-financial claims via SEC EDGAR + XBRL. This distinguishes it from sibling tools like ask_pipeworx and compare_entities, 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?

Explicitly states when to use the tool (checking truth of user statements) with examples of triggering queries. It also defines the supported scope (company-financial claims). While it doesn't provide negative usage cases or explicit alternatives, the context is sufficiently clear for an agent.

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