Uk Parliament

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

Annotations declare readOnly, openWorld, idempotent, non-destructive. Description adds: probes LLMs, scores 0-100, returns per-model details, and explains _apiKey usage with direct billing by Anthropic. No contradictions; description enriches annotation 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?

Single paragraph, no wasted words. Front-loads main function, then provides essential details on models, API key, and return structure. 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 4 parameters, no output schema, but robust annotations, the description covers return format (per-model score/confidence/signals/raw_response + combined view), use cases, and cost implications. Minor improvement could be to mention rate limits or pagination, 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%, so each parameter has a description. Description adds meaning: explains _apiKey is passed straight through to Anthropic and required only if 'anthropic' in models; notes default model is 'workers-ai' and free. Goes beyond schema with practical 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?

The description clearly states the tool probes LLMs for visibility scoring, with a specific verb 'probe' and resource 'LLMs for what they know about a business/brand/product/topic'. It distinguishes itself from sibling tools like 'ask_pipeworx' or 'bet_research' by focusing on AI visibility measurement.

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 lists use cases: AI-marketing audits, pre-launch brand checks, competitive monitoring. Explains default model and optional Anthropic probe with BYO key. Does not explicitly state when not to use, but context signals and sibling diversity make alternatives 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 already indicate readOnly, idempotent, openWorld. Description adds that it returns 'structured answer with stable pipeworx:// citation URIs' and mentions routing behavior, providing beyond-annotation context. 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?

Description is relatively long but front-loaded with purpose and usage. Each sentence adds value, though some examples could be trimmed. Appropriate for a complex meta-tool.

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 one required parameter and rich annotations. Description explains return format (citations), use cases, and routing behavior. No output schema is needed as the tool returns a structured answer with citations. Complete for a single-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 has 100% description coverage with 6 aliases for one parameter. The description does not add meaning beyond what the schema already provides for the question field. 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 routes questions to one of 3,436 tools across 780 sources, covering specific domains like SEC filings, FDA data, etc. It distinguishes itself from siblings by positioning as preferred over web search for structured data 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?

Provides explicit usage context: 'PREFER OVER WEB SEARCH' and lists trigger phrases ('what is', 'look up', etc.) with examples. Does not explicitly state when not to use but implies alternatives (web search) and gives clear 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?

Beyond annotations (readOnly, idempotent, non-destructive), the description adds key behaviors: extra LLM call cost, explicit refusal reasons, and extraction-only from tool results. 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?

Two paragraphs with front-loaded key information. Dense but clear; a minor cut in redundancy could improve, but still very effective.

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 thoroughly details return structure, refusal modes, and usage boundaries for a complex, high-stakes tool. Complete and self-contained.

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 6 parameters well-documented; description adds minimal extra semantics beyond restating purpose. 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 is a 'Hallucination-resistant answer mode for high-stakes reads' and specifies that it only uses tool result content. It distinguishes itself from the sibling 'ask_pipeworx' by noting extra cost and groundedness.

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: 'whenever an answer will be quoted, cited, or acted on' and when not: 'prefer ask_pipeworx for casual lookups.' Provides clear alternatives and 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?

The description extensively discloses behavior: fan-out to category-specific data packs, response shapes (market, analysis, evidence), resolver contract (confidence levels, alternatives), parent event extraction, news fallback handling, safety mechanisms (low-confidence short-circuit, closed market detection, wide-spread warning). These details go well beyond the annotations (readOnlyHint, idempotentHint, etc.) and provide actionable context for the 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 quite long (~500 words) and detailed, which is necessary given the tool's complexity. It is well-structured with clear sections (usage, classifiers, examples, response shapes, resolver, etc.). However, it could be more concise by trimming redundant examples or using bullet points. Front-loading the core purpose helps, but the length may overwhelm some agents.

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 is exceptionally complete given the tool's complexity (3 params, no output schema). It covers edge cases: low-confidence matches, closed markets, wide spreads, news fallback (429s), and blocking mechanisms. It explains every field in the response implicitly and provides examples of fan-outs for different bet categories. An agent can confidently invoke this tool with minimal ambiguity.

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?

All three parameters have descriptions in the input schema (100% coverage). The description adds value by explaining the market parameter's accepted formats (slug, URL, question text), the depth parameter's effect ('quick' vs 'thorough'), and include_raw's impact on response size (summary vs full payloads). This enriches the semantic 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: 'Research a Polymarket bet by pulling the relevant Pipeworx data for it in one call.' It specifies the verb (research), resource (Polymarket bet), and method (one call fetch). It distinguishes from sibling tools like polymarket_arbitrage or polymarket_edges by focusing on 'should I bet' questions and providing data packs.

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: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z".' It also provides examples and behavior for low-confidence matches and closed markets, which guide when not to fully trust results. However, it does not explicitly name alternative sibling tools for exclusion.

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 provide readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds that it returns stages, consistent with annotations. No contradiction but no additional behavioral details beyond the annotations.

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

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

Single sentence, front-loaded with key information, no extraneous 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?

Simple tool with one parameter and output schema exists. Description adequately states what the tool returns, though it omits output details. Output schema presumably 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?

Schema coverage is 0% with one parameter (bill_id). Description does not explain bill_id or how to obtain it, adding no semantic value beyond the parameter name.

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 'stages of a bill' with examples (introduction, readings, committee, royal assent), distinguishing it from siblings like get_bill (bill details) and search_bills (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?

No explicit guidance on when to use this tool versus alternatives such as get_bill or compare_entities. Context is only implied by the purpose.

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, non-destructive behavior. The description adds details: pulls latest 10-K data with off-calendar fiscal year handling, FAERS counts for drugs, sorted results, and citation URIs. 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 a single dense paragraph, but it front-loads the most critical use case with natural language triggers. Every sentence adds value, though breaking into sections could improve readability slightly.

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 thoroughly explains what each type returns (revenue, net income, etc. for companies; adverse events, approval counts for drugs) and the output format (paired data, citation URIs, sorted results). Complete for an agent to understand inputs and outputs.

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 description enriches both parameters: 'type' explained with return data, 'values' with examples (tickers/CIKs vs. drug names) and constraints (2-5 items).

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 side-by-side comparisons of 2-5 companies or drugs, with explicit examples of natural language queries. It distinguishes from sequential lookups and sibling tools like entity_profile.

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

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

The description explicitly advises to prefer this tool over sequential single-pack lookups for comparisons, giving clear usage context. It also specifies when to use company vs. drug type.

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 adds behavioral context beyond annotations: it returns top-N most relevant tools with full schemas and curated examples, ready to call without a second lookup. Annotations (readOnlyHint=true, destructiveHint=false) are consistent, and description clarifies the read-only, idempotent 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 paragraph that front-loads the key verb and purpose. It is dense and informative without excessive length, though some sentences could be slightly tighter.

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 the return format ('top-N most relevant tools with names, descriptions, and full input schemas') and that results are ready to call. However, it does not clarify how relevance is determined or ordering, leaving some ambiguity for the 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% with descriptions for each parameter. The description explains the query parameter's purpose ('Natural language description of what you want to do') and lists examples, but does not add significant semantics beyond what the schema already 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?

The description clearly states 'Find tools by describing the data or task,' specifying the verb (find/discover) and resource (tools). It distinguishes from siblings by being a discovery tool that returns top-N tools, while siblings are domain-specific 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 advises 'Call this FIRST when you have many tools available and want to see the option set,' providing clear usage context. It implies this is a preliminary step before using other tools, though it doesn't list specific exclusion criteria.

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, idempotent, non-destructive. Description adds that the tool fans out across multiple sources, patents may soft-fail due to API sunset, and details the returned data structure. No contradictions with annotations.

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

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

Description is detailed but front-loaded with examples and concise phrases. Every sentence adds value, though it is somewhat lengthy.

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 description fully explains all return fields and their sources, including fallback behavior and limitations. Complete for a complex multi-source 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%, but description clarifies that 'type' only supports 'company' and 'value' accepts ticker or CIK (not names), which adds 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?

Description clearly states the tool provides a full cross-source profile of a US public company in one parallel call. It lists specific sources and return fields, distinguishing it from chaining single-pack lookups.

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

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

Explicitly says to prefer this over chaining when a holistic view is needed, and notes that names are not supported (use resolve_entity first). Provides example queries that indicate when 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?

Annotations already provide destructiveHint=true and readOnlyHint=false. The description confirms deletion and adds context about clearing sensitive data, but does not provide significant additional behavioral detail beyond the annotations.

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

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

Two sentences plus a pairing hint; no wasteful words. The core action 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?

Simple tool with one required parameter and no output schema. The description fully covers purpose, usage, and context with siblings.

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% (the property description says 'Memory key to delete'). The tool description does not add new meaning 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 states the verb 'Delete' and the resource 'memory', with the specific key parameter. It distinguishes itself from siblings by mentioning 'remember and recall'.

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: 'when context is stale, the task is done, or you want to clear sensitive data'. Also pairs with related tools 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 declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds behavioral detail: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format. Output is a single text blob.' This explains the open-world fetch and output format, complementing the annotations.

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

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

The description is concise and front-loaded: it states the purpose, explains the extraction process, specifies the output format, and lists use cases. Every sentence adds value and there is 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 the low complexity (2 params, no output schema, no nested objects) and rich annotations, the description covers all essential aspects: what the tool does, how it works, what output to expect, and when to use it. No gaps remain.

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 does not add significant detail beyond the schema for the two parameters. It mentions 'for any URL' and implies the tool works with full URLs, but the schema already describes url and max_links 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 the tool's purpose: 'Generate a production-ready llms.txt file for any URL so AI crawlers can index the site cleanly.' It specifies the verb (Generate), resource (llms.txt file), and context (for any URL). There are no sibling tools with similar function, so it is well-distinguished.

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 specific use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' While it does not explicitly state when not to use it or name alternatives, the sibling tools are diverse and none directly compete, so the guidance is clear enough.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety and idempotence are clear. The description adds no behavioral context beyond this.

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?

While very short, the description is under-specified rather than concise; it fails to provide meaningful information in its two 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 presence of an output schema and multiple sibling tools, the description should clarify what 'detail' entails (e.g., fields returned) but does not.

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 0%, yet the description does not explain the single required parameter 'bill_id' or its expected format/source.

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 'Bill detail' is nearly tautological with the tool name 'get_bill', providing minimal specificity about what details are returned.

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 given on when to use this tool versus siblings like 'bill_stages' or 'search_bills', 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.

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds minimal behavioral context ('Member detail') beyond what annotations provide. With rich annotations, the description's contribution is adequate but not enhanced.

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 (5 words) and front-loaded with the key purpose. Every word is meaningful, with 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?

While annotations and output schema exist, the description is minimal. It does not explain what 'detail' entails, the response format, or how to use the optional 'includes' parameter. For a tool with this complexity, the description could provide more 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%, so the description does not need to add parameter details. However, it does not mention the optional 'includes' parameter or its effect. Baseline score of 3 is appropriate as the description adds no extra 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 'Member detail by member id.' clearly states the tool retrieves details about a member using their ID. It distinguishes from sibling tools like 'search_members' which implies a broader search, and 'entity_profile' which might serve a different purpose.

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

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

The description implicitly indicates usage with a member ID but does not provide explicit guidance on when to use this tool versus alternatives like 'search_members' or 'entity_profile'. No exclusions or contexts 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?

Annotations already declare readOnlyHint=true and destructiveHint=false, so the description adds value by listing return fields. It does not contradict annotations and provides context beyond what annotations offer.

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. First sentence states action and return fields, second gives usage guidance. Perfectly 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 simple list tool with one optional parameter, the description covers purpose, return fields, and use cases. No output schema is needed as fields are listed.

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 single parameter 'include_inactive' is fully described in the schema. The description does not add extra meaning but is not necessary given the high 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?

Description clearly states 'List the caller's active subscriptions' and enumerates returned fields. It distinguishes from sibling tools 'subscribe' and 'unsubscribe' by explaining its use for review and finding IDs to cancel.

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 instructs when to use: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This directly contrasts with the subscribe and unsubscribe 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?

The description discloses behavioral traits beyond annotations: it is free, does not count against tool-call quota, and is rate-limited to 5 per identifier per day. It also explains the impact (team reads digests daily, signal affects roadmap). No contradictions with annotations.

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

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

The description is moderately sized and front-loaded with the action. Every sentence serves a purpose: purpose, use cases, content guidelines, team impact, rate limits, quota note. It is well-structured 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 tool has 3 parameters, no output schema, and basic annotations, the description is complete. It covers what to include, what not to include, rate limits, and quota. It provides enough context for an agent to use the tool correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by explaining enum values in more detail and giving content guidelines (e.g., be specific, 1-2 sentences, 2000 chars max). It also warns against pasting user prompts.

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 ('Tell the Pipeworx team something is broken, missing, or needs to exist') and enumerates specific use cases (bug, feature/data_gap, praise). It distinguishes itself from sibling tools that are primarily for querying or retrieving 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 explicitly states when to use the tool: for bugs, feature requests, data gaps, or praise. It also provides guidance on content format ('Describe the issue in terms of Pipeworx tools/packs — don't paste the end-user's prompt') and mentions rate limits and quota-free 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?

Annotations already declare read-only, idempotent, non-destructive. Description adds value by explaining data source (CF analytics-engine), no PII, and caching behavior (5min-1h), going 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?

Four sentences, front-loaded with purpose, no redundant information. Each sentence contributes uniquely: purpose, use cases, data source, caching. 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 simple schema (no output schema), the description covers purpose, use cases, data source, privacy, and caching. Implicitly describes output shape (top tools, packs, volume). Could benefit from explicit return format 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 coverage is 100% (single enum parameter). Description explains the semantics of each window value ('hot right now' vs 'steady-state demand'), adding context 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 returns trending tools, packs, and call volume over specified windows. Distinguishes from siblings like 'discover_tools' and 'ask_pipeworx' by focusing on aggregated usage trends.

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?

Lists three explicit use cases: discovering hot data sources, confirming canonical tools, and aligning use cases. Provides guidance on window choice but does not explicitly compare to all 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?

Annotations indicate the tool is read-only, idempotent, and non-destructive. The description adds significant behavioral detail: it walks child markets in event mode, checks date-axis/threshold-axis ordering, computes partition_check (sum ≈1), emits trade signals for deviations >3pp, applies Jaccard similarity thresholds, and filters placeholder partitions. 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 comprehensive but verbose. It includes technical details like Jaccard similarity thresholds and partition filter criteria that could be more concisely summarized. Front-loading is good with the requirement statement, but overall density reduces readability.

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 explains the response structure: 'opportunities[]' with fields (gap_pp, suggested_trade, reasoning, monotonicity violation context) and partition_check object. All necessary behavioral and output details are covered for a complex 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 both parameters. The description adds value by explaining the context of each mode, giving concrete example slugs, and detailing the constraints (e.g., an event slug like 'fed-decision-may-2026'). It goes 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 explicitly states the tool finds arbitrage opportunities via monotonicity violations and partition-sum checks. It distinguishes between single-event and cross-event modes, and the verb-resource 'find arbitrage opportunities' is specific. Sibling tools like polymarket_edges or polymarket_kalshi_spread have different focuses, so this tool's purpose is unique.

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 opens with the requirement for one of 'event' or 'topic' (call with no args fails). It recommends 'event' for specific market slugs and 'topic' for cross-event scanning, explaining that cross-event mode catches patterns missed by single-event mode. This provides clear when-to-use 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?

Annotations indicate read-only, idempotent, and non-destructive behavior. Description significantly adds context: explains caching (1h KV), output fields (edge_pp_net, kelly_fraction, warnings), and the three segments with their logic. 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?

Description is lengthy but well-structured: front-loads purpose, explains segments, output, knobs, and diagnostics. Each sentence adds value for a complex tool. Slightly verbose but justified.

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 no output schema and 9 parameters, the description covers all essential aspects: what each model does, return format (by_segment, diagnostics), caching, and edge case handling (Fed bets, empty segments). Makes the tool's behavior fully understandable.

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 9 parameters with descriptions. The tool description enriches these by explaining tradeable-edge knobs (e.g., min_liquidity, max_spread_pp) and how min_partition_leg_kelly differs from min_kelly, adding 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?

Description clearly states the tool scans Polymarket markets for opportunities where Pipeworx data disagrees with market price. It is positioned as a discovery tool for betting decisions, distinguishing it from siblings like 'polymarket_arbitrage' by focusing on cross-data edge detection rather than arbitrage within Polymarket.

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?

Description gives clear context for use ('what should I bet on today') and explains internal segments and diagnostics, including a note on Fed bets being unreliable. However, it lacks explicit guidance on when to use this tool over specific siblings or when not to use it.

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 adds extensive behavioral details beyond annotations: explains two operating modes, response structure, safety fields (compatibility_warning, temporal_alignment, skipped counters) and their meanings. No contradiction with annotations (readOnlyHint, idempotentHint).

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 lengthy and front-loaded with the main purpose, but it repeats the pre-mapped topic list found in the schema and could be more concise. The detailed safety explanations are valuable but add verbosity.

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 response format (leg-by-leg prices, spreads, compatibility fields) sufficiently. It explains two cases for compatibility_warning and temporal alignment, making the tool behavior fully understandable.

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 3 parameters. The description adds context on mode selection and override behavior, clarifying how topic vs explicit tickers interact. Examples in schema complement the 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 computes the cross-venue spread between Kalshi and Polymarket for the same resolving question, with specific verb and resource. It distinguishes from siblings like polymarket_arbitrage by focusing on cross-venue comparison and preprocessing checks.

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?

Describes two modes (topic shortcuts and explicit tickers) and advises that pre-mapped topics often return compatibility warnings, setting realistic expectations. While it doesn't explicitly list when not to use, the safety field explanations guide appropriate 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?

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the description's burden is lower. It adds useful context about scoping ('scoped to your identifier') and the pairing with remember/forget, but does not significantly expand beyond annotations. Score 3 is appropriate as it adds some value but is not extensive.

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

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

Three sentences with no fluff. First sentence gives purpose, second explains usage context, third adds scope and pairing info. Every sentence earns its place; the description is efficiently front-loaded.

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

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

Given the tool's simplicity (1 optional param, no output schema, rich annotations), the description covers all necessary context: return behavior (value or list of keys), scope, relationship with sibling tools. No gaps identified.

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 description repeats the schema's own description of the 'key' parameter ('omit to list all keys'). The description does not add new meaning beyond what the input schema already provides, so baseline score of 3 is correct.

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 (retrieve/list) and resource (saved values/keys). It distinguishes from sibling tools 'remember' and 'forget' by mentioning them, and specifies the dual behavior (single key vs. listing all keys).

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: 'to look up context the agent stored earlier ... without re-deriving it from scratch.' Also specifies when to omit the key to list all keys. Could be improved by explicitly stating when not to use, but the description provides good guidance on context and 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?

Annotations already indicate read-only, idempotent, and non-destructive. The description adds valuable behavioral context: return fields (source, citation_uri, raw payload), how mark_read affects future calls, and that polling works. This goes beyond annotations without 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?

The description is 4 sentences, covering purpose, return fields, filtering options, and alternative access. It is concise but includes useful examples. Could be slightly tighter, but overall efficient and front-loaded.

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

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

Given 5 optional parameters, no output schema, and annotations, the description adequately explains the tool's functionality: what it returns, how to filter, and behavior of mark_read. It provides enough context for an agent to select and 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?

All 5 parameters are fully described in the schema (100% coverage). The description adds extra meaning: examples for 'type' (sec_8k), clarifies 'since' as ISO timestamp, and explains 'mark_read' effect. This enhances understanding beyond base 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 pulls fired events from a subscription feed and returns recent alerts. The verb 'pull' and resource 'fired events' make the purpose specific, but it does not explicitly contrast with sibling tools like 'list_subscriptions' or 'recent_changes', so it lacks differentiation.

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 usage context: filtering by type and/or since, using mark_read, and polling. However, it does not give explicit when-to-use or when-not-to-use guidance compared to sibling tools. Alternative access is mentioned (registry URL), but not tool 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?

Beyond annotations (readOnlyHint, etc.), the description details fan-out to multiple sources, fallback logic, soft-fail for USPTO, and return structure (grouped by source with counts and citation URIs). 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?

Description is relatively long but well-structured, starting with purpose and then detailing parameters and behavior. Each part adds value. Could be slightly more concise, but 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 no output schema, the description explains the return format. All three required parameters are explained, annotations cover behavior, and it provides usage guidelines and limitations. Fully adequate for complex 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 already describes all parameters (100% coverage). The description adds context: 'since' accepts ISO date or relative shorthand with examples, 'value' explained as ticker or CIK, 'type' only company. Adds good user guidance.

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 change feed for a company in a time window, fanning out to multiple sources (SEC, GDELT/GNews, USPTO). It distinguishes from the sibling entity_profile by specifying when to use that tool instead.

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 when to use this tool (for recent changes) vs entity_profile (for static profile). Provides example queries and explains fallback behavior (GDELT→GNews) and future limitations (USPTO sunset).

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 destructiveHint, so the description does not need to repeat these. It adds no behavioral context beyond 'recorded', which is already obvious. 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 extremely concise with no wasted words. It is front-loaded and efficient, though very short.

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?

While output schema exists and the tool is simple, the description provides minimal context about what 'divisions' are (parliamentary votes) and no example usage. It is adequate but not enriched.

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

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

Schema description coverage is 100% with clear descriptions for all three parameters (take, house, date_from). The description adds no additional meaning; baseline 3 is appropriate.

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

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

The description 'Recent recorded votes (divisions)' identifies the resource (divisions/votes) but lacks a verb and does not distinguish it from siblings like search_bills or bill_stages. It is clear enough but minimal.

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. The description does not mention when not to use it or any prerequisites, which is insufficient for an agent to make a confident choice.

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?

Description adds significant behavioral context beyond annotations: scoped by identifier, persistence differences between authenticated (persistent) and anonymous (24 hours) users. Annotations already declare idempotentHint=true and destructiveHint=false, which description confirms.

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?

Description is concise (5 sentences) with no redundant information. First sentence states purpose, then expands with usage context, behavior, and pairing. 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?

Description fully explains behavior for a simple key-value store, including scoping, persistence, and pairing with recall/forget. No output schema exists, but description does not need to explain return values for a storage action.

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 both parameters (key, value) with descriptions. Description adds value with usage examples (e.g., 'subject_property', 'target_ticker') and explains scoping, but schema already defines types and required status.

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 saves data for later reuse, using a specific verb ('Save data') and resource (key-value memory). It explicitly distinguishes from sibling tools 'recall' and 'forget' by mentioning them as pairings.

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?

Description provides clear guidance on when to use ('when you discover something worth carrying forward') and pairs with recall/forget, but does not explicitly state when not to use (e.g., for transient or large data).

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 readOnly, idempotent, and non-destructive behavior. Description adds that the tool cascades through multiple endpoints, replacing 2-3 manual lookups, which provides useful context 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?

Description is concise but packs substantial information. Could be slightly better structured, but it is efficient and front-loaded with key usage guidance.

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 the description fully explains return values for both entity types, including identifiers and citation URIs. Covers supported types, input formats, and disambiguation behavior.

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 100% of parameters. Description adds examples and explains auto-disambiguation for company type, enhancing 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 specifies that the tool resolves names to official identifiers, with concrete examples (ticker, CIK, RxCUI). It distinguishes itself from sibling tools by focusing on entity 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?

Explicitly states 'Use FIRST whenever you have a name but need an ID.' Provides supported types and input formats. Lacks explicit when-not-to-use instructions, 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?

The annotations already indicate read-only, idempotent, non-destructive behavior. The description adds that it internally calls ai_visibility_check and returns rankings, confidence, and signal density. No contradictions, but the added value beyond annotations is moderate.

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: the first explains the core functionality, the second provides a use case and output summary. No waste, front-loaded with essential 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?

The description covers input, internal behavior (probes each entity), and output structure (ranked list with score, confidence, signal density). It also explains the context parameter. No output schema exists, but the description sufficiently describes 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% with descriptions. The description adds that the first entity in 'entities' is treated as the subject for narrative purposes, which is not in the schema. This extra semantic detail justifies a score above baseline.

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

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

The description clearly states the tool compares AI visibility across multiple entities, probes each with ai_visibility_check, ranks by score, and returns a ranked list. It differentiates from the sibling ai_visibility_check by indicating it handles multiple entities side-by-side.

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 when to use it: for competitive AI-marketing audits comparing brand recognition. It provides an example question. It does not explicitly state when not to use it, but the context is clear enough for an agent to decide.

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 partial failures, bundlephobia first measurement latency (5-30s), and sources_failed reporting. Annotations already indicate readOnlyHint/idempotentHint, but description adds significant context 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?

Description is relatively long but efficient, front-loading the main purpose. Every sentence adds value; could be slightly more concise but still effective.

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 composite nature and no output schema, description lists exact return fields (is_latest, license, etc.) and mentions per-advisory detail, links, and alternative versions. Covers all necessary 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% with descriptions for both parameters. Description adds context: version defaults to latest when omitted, and gives example of scoped packages like '@types/node'.

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 is a composite check for npm packages, combining deps.dev and bundlephobia. It distinguishes from sibling tools by specifying the npm ecosystem and noting alternatives for other ecosystems.

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 when to use: 'is X safe / popular / small' or 'what does adding lodash cost me'. Also provides exclusion: 'NPM ecosystem only in v1; PyPI / Maven / Cargo / Go fall under deps.dev:version directly.'

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, idempotent behavior. Description adds no further behavioral context beyond listing searchable fields. Minimal addition, but 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?

Single sentence, front-loaded with purpose. Could be slightly clearer (slashes ambiguous), but overall concise and efficient.

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

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

Given 8 optional parameters and no explanation of filter combination (AND/OR), description feels incomplete. Output schema exists but description should provide 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%, so baseline is 3. Description does not add extra meaning beyond listing parameters; no guidance on combination logic or constraints.

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?

Clear verb 'search' and resource 'bills' with specific filter criteria (title, session, stage, sponsoring member). Distinguishes from siblings like search_hansard and search_members.

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?

Description implies usage for searching bills with specified filters, but does not provide explicit when-to-use or when-not-to-use guidance nor mention alternatives. Adequate for basic understanding.

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

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false. The description adds no behavioral context beyond what is already in annotations or 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 concise (one sentence) but lacks depth. It is appropriately sized for minimal information but does not front-load distinguishing 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?

Given the rich schema and annotations, the description is adequate but does not explain what Hansard is, result ordering, or scope, leaving some gaps for a complete 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?

Schema coverage is 100% with clear parameter descriptions. The description adds no additional meaning beyond what the schema provides, meeting the baseline expectation.

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' and the resource 'debate contributions in Hansard', but does not distinguish from sibling tools like search_bills or search_members.

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 vs alternatives. The description lacks context for appropriate usage 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?

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so safety profile is covered. The description adds no behavioral context beyond the parameter list, such as default filtering (is_current defaults to true) or result structure. It meets the minimum but adds little value.

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 8 words. It front-loads the action and resource, making it immediately scannable and efficient.

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

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

Given the tool has 7 parameters and an output schema, the description covers the core functionality but omits details like default is_current behavior, pagination, and result format. While the schema covers these, the description lacks enough context to fully understand usage at a glance.

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 description does not need to explain parameters in detail. The description merely lists a subset of parameters (name, party, location, house) without adding new meaning or constraints 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?

The description clearly states it searches MPs and Lords by name, party, location, and house. This specific verb and resource combination distinguishes it from sibling tools like search_bills and search_hansard, making the purpose immediately 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?

The description provides no guidance on when to use this tool versus alternatives, nor any context about prerequisites or ideal scenarios. It simply states what it does without suggesting when it is appropriate or not.

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?

Beyond annotations (readOnlyHint, idempotentHint, etc.), the description adds significant behavioral details: uses BGE-base-en embeddings + cosine similarity over 500-char overlapping windows, cap of 200K chars with truncation and flagging, and returns passages with character offsets and similarity scores. 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 paragraph but well-structured: core purpose in the first sentence, then usage guidance, benefits, pairing hint, and technical details. Every sentence provides unique information without redundancy, making it efficient for an agent 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 moderate complexity (3 parameters, no output schema), the description covers all essential aspects: what it does, when to use it, technical implementation (embeddings, window size, similarity metric), limitations (character cap), and return format (passages with offsets and scores). This completeness enables an agent to invoke it correctly without ambiguity.

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 specifying the character limit for 'text' (max ~200K chars), providing a natural-language query example for 'query', and clarifying that 'limit' has a default of 5 and range 1-20. This extra context justifies a score above baseline.

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

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

The description clearly states the tool performs semantic search inside a fetched record, using a natural-language query to return top-N passages with offsets and similarity scores. It distinguishes from siblings by explicitly pairing with ask_pipeworx_grounded and clarifying that it saves context by returning only relevant passages instead of the whole document.

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

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

The description explicitly advises when to use the tool: 'Use when the record is too big to cram into the prompt.' It also mentions pairing with ask_pipeworx_grounded for a grounded workflow. However, it does not explicitly state when not to use it or list alternatives beyond that sibling, though the intent 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?

Provides extensive behavioral details: OAuth requirement, subscription types with example parameters, delivery channels with caps and verification steps. However, there is a contradiction with the idempotentHint annotation (true) since 'new subscription id' implies non-idempotent creation.

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?

Well-structured with key purpose upfront, followed by clear sections for types, delivery, and prerequisites. Could be slightly shorter, but every sentence adds essential information without 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?

Covers all input parameters thoroughly, explains return value (new subscription id), and addresses constraints (OAuth requirement, SMS cap, phone verification). Despite no output schema, the description is sufficient for a creation 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%, and the description significantly enriches each parameter with examples, constraints (e.g., SMS cap, phone verification), and type-specific details (e.g., items codes for sec_8k). Goes well 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 uses specific verb 'Create' with clear resource 'proactive monitoring subscription' and distinct list of supported types. It distinguishes from sibling tools like list_subscriptions, unsubscribe, and recent_alerts by focusing on creation.

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 the purpose and prerequisite: 'Requires a Pipeworx OAuth account'. Lists supported types and delivery channels, guiding when to use each. Does not explicitly state when not to use, but context from siblings is implied.

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

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

Discloses key behaviors: ownership enforcement, deactivation vs deletion, and link to recent_alerts. Annotations (destructiveHint false, readOnlyHint false) align with description. OpenWorldHint true suggests possible undocumented side effects, but description covers main 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?

Two efficient sentences that front-load the action and include all key information without 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 one parameter, no output schema, and annotations, the description fully covers what the agent needs to know: purpose, constraints, and outcome (deactivation, not deletion). No 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?

Single parameter 'id' with schema description 'Subscription id (uuid) returned by subscribe.' This adds context beyond the schema (origin of the id). Schema coverage is 100%, so baseline is 3; description adds 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?

Explicitly states 'Cancel a subscription by id' and distinguishes from sibling tools like subscribe. Mentions ownership enforcement and that the row is deactivated, not deleted, providing clarity on the exact operation.

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

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

Provides clear when-to-use guidelines: ownership is enforced (only cancel own subscriptions) and the row is deactivated (not deleted) so historical events remain available via recent_alerts. This helps the agent decide when to use this tool vs 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?

Annotations already indicate readOnly, openWorld, idempotent, and non-destructive behavior. The description adds valuable context: it returns a verdict, extracted structure, actual value with citation, and percent delta, and mentions that it replaces 4-6 sequential calls. 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 informative without being overly verbose. It front-loads usage contexts and examples, then specifies domain and output. Every sentence provides value, though a slight trim 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 tool's complexity and lack of output schema, the description thoroughly explains the return values and behavior. It covers domain, limitations ('v1 supports company-financial claims'), and examples. It is complete enough for effective use.

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

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

The sole parameter 'claim' has a clear schema description (100% coverage). The description enhances it with examples of natural-language claims and explains the expected format, adding 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 tool's purpose: natural-language claim verification against authoritative sources. It provides concrete examples like 'Is it true that...' and 'verify the claim that...', and specifies the domain (company-financial claims via SEC EDGAR + XBRL). This distinguishes it from sibling tools which are mostly search or entity-related.

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 to use the tool when the agent needs to check factual correctness of something a user said. It gives usage contexts (e.g., 'did X really...'). However, it does not explicitly state when not to use it or list alternatives, though the domain restriction is clear.

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