Openfigi

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

Annotations already declare readOnlyHint, openWorldHint, destructiveHint. Description adds context about returning structured answers with stable pipeworx:// citation URIs and filling arguments. 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?

Front-loaded with key usage instruction ('PREFER OVER WEB SEARCH'). The list of use cases is comprehensive but slightly long; each sentence earns its place. Well-structured.

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 present, description is complete enough. Explains tool function, scope, and output format (citations). Could mention potential latency but not necessary.

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 'question' parameter with good schema description. Schema coverage is 100%, so baseline is 3. Description adds value by listing example question types and explaining how the tool processes them.

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 appropriate tools from over 1,423 sources, returning structured answers with citations. It provides specific examples like 'current US unemployment rate' and distinguishes itself from sibling tools like '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?

Explicitly says 'PREFER OVER WEB SEARCH' and lists many domains (SEC filings, FDA data, etc.). It advises use for factual questions even if web search could answer. Lacks explicit when-not-to-use but covers broad 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 adds significant behavioral context beyond annotations: it explains that the tool resolves the market, classifies the bet, fans out to appropriate data packs, and returns an evidence packet with a market-vs-model comparison. It also describes the 'depth' parameter (quick vs thorough). Annotations (readOnlyHint, destructiveHint, openWorldHint) are consistent and not contradicted.

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

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

The description is well-structured: it starts with the core action, then explains inputs, process, and use cases. It is front-loaded and every sentence adds value. However, it is slightly verbose with the promotional last sentence about being the 'core demo product', preventing a perfect 5.

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 purpose, input, behavior, and use cases. However, there is no output schema, and the description only vaguely mentions a 'evidence packet plus a simple market-vs-model comparison'. For a research tool, agents would benefit from more detail about the output structure or fields. Thus, completeness is adequate but not thorough.

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

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

The input schema provides 100% coverage for both parameters with clear descriptions. The tool description restates the input formats for 'market' but adds no new meaning beyond the schema. For 'depth', the schema already explains quick vs thorough. Hence, the description adds minimal extra value beyond what is already in the schema, achieving the baseline of 3.

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 resource (Polymarket bet) and action (research/pull data), and distinguishes it from siblings like polymarket_arbitrage and polymarket_edges, which focus on different aspects.

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

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

The description explicitly suggests use cases: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?"' and notes that agents using this tool convert better than those discovering packs independently. However, it does not explicitly name alternative sibling tools, so it lacks direct comparisons.

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 and non-destructive behavior. The description adds valuable context: data sources (SEC EDGAR/XBRL for companies, FAERS/FDA for drugs) and output format (paired data with citation URIs). It also highlights efficiency gains (replacing 8–15 calls), going well 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?

The description is one focused paragraph with clear front-loading: first sentence states purpose, followed by usage patterns, then per-type behavior, and finally a benefit statement. Every sentence adds value; 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 tool's complexity (multiple entity types, several financial and clinical data points) and lack of output schema, the description covers all critical aspects: type variants, data sources, output nature (paired data with citations), and use case triggers. It is sufficiently complete for an agent to assess when and how to invoke it.

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

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

Schema coverage is 100%, but the description adds crucial context: for 'type', it clarifies that 'company' and 'drug' map to specific data sources; for 'values', it gives concrete examples (tickers like AAPL, drug names like ozempic) and reinforces the 2–5 range. This enhances what the schema already provides.

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

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

The description specifies a clear verb 'compare' with defined resources 'companies or drugs', and explicitly lists user query patterns ('compare X and Y', 'X vs Y', etc.). It distinguishes itself from sibling tools like entity_profile and search by focusing on side-by-side comparison across multiple entities.

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

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

The description provides explicit triggers for when to use the tool (user says compare, vs, stack up, etc.) and explains the behavior for each type. However, it does not state when not to use it or name alternative tools for single entity queries, missing full coverage of when-not 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 declare readOnlyHint=true and destructiveHint=false, so the description does not need to repeat safety traits. The description adds behavioral context: 'Returns the top-N most relevant tools with names + descriptions.' This clarifies the output format. It could mention ordering or pagination, but given the annotations, this is sufficient.

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

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

The description is two sentences, front-loaded with the main verb and resource. The first sentence is a bit long due to listing many domains, but it is still concise. Every part adds value, though the list could be slightly more structured. It earns a 4 for being efficient without being terse.

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

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

For a simple tool with 2 parameters, no output schema, and no nested objects, the description is fully complete. It covers the purpose, when to use, parameter hints, and return behavior. The annotations already handle safety. 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 description coverage is 100%—both parameters have descriptions. The description adds context: 'Returns the top-N most relevant tools' for the 'limit' parameter and 'Natural language description of what you want to do' for 'query'. However, it largely reinforces the schema rather than adding new meaning. Baseline for 100% coverage is 3.

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

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

The description clearly states the tool's purpose: 'Find tools by describing the data or task.' It provides a comprehensive list of domains (SEC filings, financials, FDA drugs, etc.) and examples, which helps the agent understand the scope. It also distinguishes itself from sibling tools by advising to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).'

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: 'Use when you need to browse, search, look up, or discover what tools exist for:' followed by detailed domains. It also gives a specific hint: 'Call this FIRST when you have many tools available and want to see the option set.' However, it does not explicitly state when NOT to use it (e.g., when the tool is already known), which would make it a 5.

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 openWorldHint=true. The description adds that the tool returns pipeworx:// citation URIs and covers return categories, but lacks details on size limits or error handling.

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 (~100 words), front-loaded with purpose and usage, lists return data efficiently, and avoids redundancy with the schema.

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 complex aggregation tool without an output schema, the description lists all return categories. However, it omits pagination, error conditions, or rate limits, but is still sufficient for most use cases.

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

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

Schema coverage is 100%, and the description adds essential context: 'type' is limited to 'company', 'value' accepts ticker or CIK, and names need resolve_entity first.

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 'Get everything about a company in one call' and lists specific use cases and return data, distinguishing it from siblings like 'compare_entities' or 'resolve_entity'.

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

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

Explicitly tells when to use (e.g., 'tell me about X' queries, avoiding 10+ tool calls) and when not (if only a name, use resolve_entity first), including input format constraints.

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 and non-destructive behavior. The description adds that the tool filters by idType and value, providing specific behavioral context without contradicting 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, concise sentence that conveys the core purpose without extraneous words, earning its place.

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

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

With no output schema and four parameters, the description covers only half the parameters and omits details on return values or behavior of currency/exchCode, making it incomplete for agent use.

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

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

Only two parameters (idType, value) are explained in the description. The schema has four parameters with 0% coverage, and the description ignores currency and exchCode, leaving their meaning unclear.

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

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

The description clearly states it filters search by idType and value, and notes it's a subset of /search. This distinguishes it from the broader search tool but not from other siblings like bet_research.

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

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

The description implies usage is for filtering search results by referencing /search as the superset, but no explicit when-to-use or when-not-to-use guidance is given.

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

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

Annotations already flag destructive behavior. The description adds useful context about why deletion is appropriate (stale, sensitive data), without contradicting 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 concise sentences, front-loaded with action and purpose. No unnecessary words.

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

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

For a simple one-parameter tool, the description covers purpose, usage guidance, and relationships. No output schema needed; completeness is high.

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

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

The schema fully describes the single parameter 'key'. The description adds no additional semantic information beyond what the schema provides, so baseline score applies.

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

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

The description clearly states the tool deletes a memory by key, with a specific verb and resource. It also hints at relationships with siblings 'remember' and 'recall', aiding 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?

Explicitly lists use cases: when context is stale, task done, or clearing sensitive data. Also suggests pairing with related tools, providing clear 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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds that the output is FIGIs, which is useful beyond annotations. However, it does not detail response format or potential limitations.

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

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

The description is a single, front-loaded sentence with no unnecessary words. It efficiently conveys the core functionality.

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

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

Given the simple input schema and absence of output schema/enums, the description adequately covers the tool's purpose and input structure. However, it lacks details on output format or edge cases, which slightly reduces completeness.

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

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

Schema coverage is 100%, and the description adds value by specifying the output (FIGIs) and the structure of the 'jobs' array. This enhances understanding beyond the schema's parameter descriptions.

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

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

The description clearly states the action ('Map'), the input ('a batch of instrument-id queries'), and the output ('FIGIs'). It is specific and distinguishes the tool from siblings like 'resolve_entity' or 'search'.

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

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

The description implies usage for converting instrument IDs to FIGIs but provides no explicit when-to-use guidance, exclusions, or alternatives. The context is clear but lacks comparative direction.

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?

Adds context beyond annotations: it's free, rate-limited to 5 per identifier per day, doesn't count against tool-call quota, and the team reads digests daily affecting roadmap. 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?

Single paragraph, front-loaded with purpose, then guidance, then constraints. Every sentence is meaningful and concise.

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

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

Given 3 params with full schema coverage and no output schema, the description covers usage, constraints, rate limits, and expected content. Agent can fully decide when and how to 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?

Input schema has 100% coverage with descriptions. The description adds value by explaining enum values (bug, feature, etc.) and giving usage examples, but the schema already does a lot.

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 'tell' and the resource 'Pipeworx team' about bugs, features, data gaps, or praise. It distinguishes from siblings like 'search' or 'filter' by being a feedback channel.

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: bug, feature/data_gap, praise, other. Provides guidance on describing issues in terms of tools/packs, and notes what not to do (don't paste end-user prompt). Also mentions rate limits and quota.

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, and destructiveHint false. The description adds behavioral context such as searching and grouping related markets, checking monotonicity, and returning ranked opportunities. No contradicting information.

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 yet comprehensive: it states the purpose, outlines two modes with examples, and mentions return format. Every sentence adds value, and the structure is well-organized with clear labeling of modes.

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 having no output schema, the description explains what the tool returns (ranked opportunities with trade direction and reasoning). It covers both modes, provides usage context, and clarifies edge cases. Complete for the tool's complexity.

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

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

The input schema has 100% description coverage for both parameters. The description further adds value by explaining the two modes (event and topic) and how each parameter is used, including example inputs like event slug or seed question.

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 by checking monotonicity violations. It clearly distinguishes two modes (event and topic) with specific use cases, making the purpose highly clear and actionable.

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

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

The description provides explicit guidance on when to use each mode: event mode for a single event slug, topic mode for cross-event searches. It explains why cross-event mode is necessary for cases like cutoff dates in separate events, including a concrete example.

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 details the internal workflow: scanning top markets, grouping by asset, fetching price history once (avoiding redundant API calls), computing model probability per market, ranking by absolute edge, and returning top N with suggested trade direction. This goes well beyond the annotations (readOnlyHint, openWorldHint) and the input schema, providing full transparency.

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

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

The description is front-loaded with the core purpose and then adds necessary detail in subsequent sentences. It is slightly verbose but every sentence adds value (e.g., explaining the model, the grouping, the ranking). Could be trimmed slightly but remains efficient for the complexity.

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 no output schema, the description adequately explains what is returned (top N edges with edge magnitude and suggested trade direction). It also covers the scope (crypto-price bets, V1) and the overall workflow. The annotations and schema parameters are well complemented.

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

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

Although the input schema has 100% description coverage for parameters, the description adds default values (limit=10, window='1wk', min_edge_pp=0.5) and clarifies that 'edge' is measured in percentage points. It also explains that 'limit' corresponds to 'Top N edges', tying the parameter to the output. This adds meaningful 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 the tool scans Polymarket markets and returns edges where Pipeworx data disagrees with market price. It uses specific verbs ('scan', 'return') and defines the resource (Polymarket markets with Pipeworx data). It distinguishes itself from sibling tools like 'polymarket_arbitrage' by focusing on model disagreement.

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 the tool is built for the 'what should I bet on today' question and helps users discover opportunities without manual browsing. It provides context on when to use it, but does not explicitly mention when not to use it or compare to alternatives like 'polymarket_arbitrage'.

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; description adds context about identifier scoping and key omission 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?

Three sentences, front-loaded with core action, no wasted words.

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

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

Given the tool's simplicity (1 optional param, no output schema), the description covers behavior, scoping, and relationships completely.

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 description adds little beyond the schema's parameter description. Baseline 3 is appropriate.

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

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

Description clearly states the tool retrieves previously saved values or lists all keys, distinguishing it from siblings 'remember' and 'forget'.

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

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

Explicitly says when to use (look up context stored earlier) and implicitly when not, and mentions pairing with remember/forget.

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

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

Annotations already indicate read-only and non-destructive behavior. The description adds that it fans out to three external sources, returns structured changes with count and citation URIs, and explains the 'since' parameter format in detail.

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

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

The description is concise, front-loading purpose and usage, then adding technical details. Every sentence adds value; no filler.

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

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

Given the tool's complexity (three data sources, multiple parameter formats), the description covers purpose, input format, return structure, and usage examples comprehensively. No missing critical 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 parameter descriptions, but the description adds practical usage guidance (e.g., 'use "30d" for typical monitoring') and clarifies input formats (ticker or CIK for value, ISO or relative for since).

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 starts with 'What's new with a company' and provides concrete user query examples. It clearly states the tool fans out to SEC, GDELT, and USPTO, distinguishing it from siblings like search or 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 lists use cases with example queries ('what's happening with X?', 'any updates on Y?') and suggests typical usage ('for typical monitoring'). It does not explicitly state when not to use or name alternatives, but the guidance 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?

Discloses scoping by identifier, authentication persistence, and 24-hour session retention. Adds behavioral context beyond annotations (readOnlyHint=false, destructiveHint=false).

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, front-loaded with purpose. No wasted words.

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

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

For a simple key-value store tool with 2 fully described params and no output schema, the description covers usage, persistence, and related tools comprehensively.

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 parameter descriptions. Description does not add new parameter-specific info beyond schema; baseline 3 is appropriate.

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

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

Clearly states 'Save data the agent will need to reuse later' and specifies key-value store. Distinguishes from siblings 'recall' and 'forget'.

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

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

Explicitly tells when to use: 'when you discover something worth carrying forward'. Provides examples and mentions pairing with recall and forget.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by disclosing the output: 'Returns IDs plus pipeworx:// citation URIs.' It does not contradict annotations and provides additional behavioral context beyond the structured fields.

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

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

The description is concise and well-structured: a clear purpose statement, usage context, examples, and additional notes. Every sentence adds meaningful information without filler. It is front-loaded with the core purpose, making it efficient for agents 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 (2 parameters, no output schema), the description is highly complete. It covers the tool's purpose, inputs, outputs, usage timing, and examples. The presence of strong annotations reduces the need for further behavioral details, and the description effectively fills the gaps.

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

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

Schema coverage is 100% with descriptions for both parameters. The description goes beyond the schema by providing examples (e.g., 'Apple' → AAPL) and clarifying acceptable inputs for the 'value' parameter (ticker, CIK, name). This enhances meaning without redundancy, warranting a score above the baseline of 3.

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

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

The description clearly states the tool's purpose: 'Look up the canonical/official identifier for a company or drug.' It specifies the ID systems (CIK, ticker, RxCUI, LEI) and distinguishes from sibling tools by positioning itself as a prerequisite for tools needing official identifiers. The statement 'Replaces 2–3 lookup calls' further clarifies its consolidated role.

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

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

Explicit guidance on when to use: 'Use when a user mentions a name and you need the CIK...' and 'Use this BEFORE calling other tools that need official identifiers.' This provides clear context and sequential ordering, helping the agent decide when to invoke this tool over 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 adds no behavioral transparency beyond the annotations. Annotations already indicate a read-only, non-destructive operation, but the description fails to disclose any additional traits such as pagination, result limits, or supported text formats.

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 short (one sentence), which is concise but sacrifices informativeness. It is front-loaded but does not earn its place by being overly terse.

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

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

Given the tool has 5 unelaborated parameters, no output schema, and no enums, the description is grossly incomplete. It does not explain what constitutes an 'instrument', how text search functions, or what the optional filters contribute.

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 description does not elaborate on any of the 5 input parameters. With 0% schema description coverage, the description provides no guidance on the usage of 'query', 'currency', 'exchCode', 'marketSecDes', or 'securityType', leaving the agent to infer meaning.

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

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

The description 'Text search across instruments' clearly states the action (search) and resource (instruments), distinguishing it from sibling tools like filter or map. However, 'instruments' is somewhat vague without further context.

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

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

No usage guidelines are provided. The description does not specify when to use this tool over alternatives, nor does it mention any exclusions or prerequisites.

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=True and destructiveHint=False. The description adds valuable behavioral context: it returns a verdict with specific categories, includes citation links and percent delta, and states it supports v1 with a defined scope. 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 a single paragraph that front-loads the purpose and usage. Every sentence adds value, but it could be slightly more concise. The structure is logical: purpose, usage, scope, return value.

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

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

Given the tool's simplicity (single parameter, no output schema, rich annotations), the description is fully complete. It covers what the tool does, when to use it, its current limitations, and the return format, leaving no ambiguity for an AI agent.

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

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

The input schema has 100% description coverage for the single parameter 'claim', with example values. The description adds extra semantic value by clarifying the expected format and domain of claims (financial claims for public US companies), going beyond the schema's generic example.

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 validates factual claims, using specific verbs like 'fact-check' and 'confirm/refute'. It explicitly names the resource as natural-language claims against authoritative sources. It also distinguishes itself from siblings by noting it replaces multiple sequential calls.

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

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

The description provides explicit usage guidance: 'Use when an agent needs to check whether something a user said is true' and gives example queries. It also specifies the supported domain (company-financial claims via SEC EDGAR + XBRL), indicating when not to use it for other claim types.

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