Lens Org

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

Beyond the readOnlyHint annotation, the description reveals that the tool routes queries to one of 2,353 tools, fills arguments automatically, and returns structured answers with pipeworx:// citation URIs. This adds meaningful behavioral context about the routing and citation behavior not captured by 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 front-loaded with 'PREFER OVER WEB SEARCH' and efficiently lists examples. While it packs much information into a single paragraph, it could be slightly more concise by removing redundant phrases. However, it remains well-structured for an AI 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?

For a simple tool with one parameter and no output schema, the description adequately explains its routing function and return format (structured answer with citations). However, it lacks details on error handling, ambiguous queries, or cases where no tool matches, leaving some gaps in 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?

With 100% schema description coverage, the baseline is 3. The description adds value by specifying the domains and types of questions (e.g., 'current US unemployment rate') that are appropriate, going beyond the generic 'Your question or request' schema description.

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

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

The description clearly states it routes questions to a vast array of authoritative tools and returns structured answers with citations. It explicitly says 'PREFER OVER WEB SEARCH' and lists numerous domains (SEC filings, FDA data, etc.), distinguishing it from general web search and indicating its specific 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 provides strong usage guidance by stating to prefer this tool over web search for factual questions and gives query patterns like 'what is', 'look up', etc. It does not explicitly contrast with sibling tools like scholarly_search, but the broad coverage and emphasis on structured data adequately guide the agent.

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

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

Annotations already indicate readOnlyHint=true and openWorldHint=true. The description adds significant behavioral context: it resolves the market, classifies the bet, fans out to relevant packs, and returns an evidence packet with a comparison. No contradictions exist, and the description provides details beyond what annotations convey.

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, moderately long paragraph, but every sentence adds value. It is front-loaded with the core purpose. Slightly longer than necessary, but not wasteful. Could be split into two sentences for readability, 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?

With no output schema, the description fully explains the return value (evidence packet and market-vs-model comparison). It covers the fan-out logic based on bet classification. For a tool with only 2 parameters and no output schema, the description is remarkably complete, giving the agent all needed 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 'market' and 'depth'. The description adds value by explaining that 'market' can be a slug, URL, or question text, and that 'depth' defaults to 'thorough'. It also clarifies how the tool uses these parameters (resolves, classifies, fans out).

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 inputs (slug, URL, question) and outputs (evidence packet, comparison). It distinguishes itself from sibling tools by noting it's the 'core demo product.'

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 when to use: 'Use for "should I bet on X?", "what does the data say about this Polymarket market?", or "is there edge in this bet?".' It also contrasts with agents that have to discover packs themselves, providing clear guidance on when this tool is appropriate.

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

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

Annotations indicate readOnlyHint=true and no destructive behavior. The description adds significant detail: it pulls data from SEC EDGAR/XBRL for companies and FAERS for drugs, returns paired data with citation URIs, and replaces many calls. 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 concise with about 5-6 sentences, front-loaded with the core purpose. Each sentence adds useful information, though it could be slightly tighter without losing clarity.

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

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

The description covers the tool's dual type support, data sources, input range, and output format (paired data + citation URIs). It is comprehensive for a tool without an output schema, though the structure of returned data could be more explicit.

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

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

The schema already has 100% description coverage for both parameters. The description adds value by providing concrete examples (e.g., 'AAPL, MSFT' for company and 'ozempic, mounjaro' for drug) and clarifying the format of values.

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with specific use cases like 'compare X and Y'. It distinguishes between entity types and mentions data sources. It also differentiates from siblings by noting it replaces many 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 explicitly lists trigger phrases (e.g., 'compare X and Y', 'X vs Y') and when to use the tool. However, it does not explicitly exclude scenarios or compare with sibling tools like entity_profile or scholarly.

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 mark the tool as read-only and non-destructive. The description adds clarity by explaining the return format ('top-N most relevant tools with names + descriptions') and ordering, which goes beyond what annotations convey. However, it does not specify if results are live or cached, or how relevance is determined.

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, followed by usage guidance, examples, and return behavior. It is relatively concise given the amount of context provided, though the list of domains adds length. Every sentence adds value.

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

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

For a discovery tool without an output schema, the description adequately explains what is returned (top-N tools with names and descriptions) and when to invoke it. The parameter details are covered by the schema, and the behavioral context (first step, listing options) is fully provided. 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?

The input schema fully describes both parameters (query and limit) with clear descriptions. The tool description does not add additional semantic information about the parameters beyond what the schema provides. With 100% schema coverage, 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 identifies the tool as a discovery mechanism ('Find tools by describing the data or task'). It lists specific domains and distinguishes this from sibling tools by explicitly stating its role as a meta-tool for browsing available options.

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') and advises calling it FIRST to see the option set. It provides concrete examples of use cases, leaving no ambiguity about its purpose relative to 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 openWorldHint=true. The description adds context beyond annotations by detailing what 'everything' includes (filings, fundamentals, patents, news, LEI) and mentions citation URIs. It does not contradict annotations; the additional behavioral context is valuable though not exhaustive (e.g., no mention of rate limits or response structure).

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 ('Get everything about a company in one call') and uses five sentences to cover use cases, returned data, and parameter guidance. Every sentence contributes information; there is no redundancy. Slightly longer than minimal but still 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's complexity (aggregating multiple sources) and absence of output schema, the description adequately explains what is returned (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. It does not specify output structure or potential limits (e.g., number of filings), but the overview suffices for an agent to understand the tool's capability. Missing a note on pagination or response format prevents a perfect score.

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 both parameters described. The description adds significant value: for 'type', it clarifies that only 'company' is supported; for 'value', it elaborates on acceptable inputs (ticker or zero-padded CIK) and explicitly warns that names are not accepted, directing to resolve_entity. This goes beyond the schema's descriptions, justifying 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?

Description clearly states the tool retrieves comprehensive company data (SEC filings, fundamentals, patents, news, LEI) in one call, with specific verb 'Get everything about a company'. It distinguishes itself from sibling tools by naming the alternative approach of calling 10+ pack tools, and provides concrete user query examples, making the purpose unmistakable.

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 a user asks for company profile or to avoid multiple tool calls. Also specifies when not to use: if only a name is available, directing the agent to use resolve_entity first. This provides clear decision boundaries and names an alternative, meeting the highest standard.

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 destructiveHint=true. The description confirms deletion and adds context about why to delete, but does not disclose additional behavioral traits beyond what annotations provide.

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

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

Two sentences with no wasted words. The first sentence immediately states the action, making it easy to grasp.

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 delete tool with one parameter and no output schema, the description covers purpose, usage, and context thoroughly.

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 does not add extra meaning to the 'key' parameter beyond the schema's own description ('Memory key to delete'). 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 verb 'Delete', the resource 'memory', and the method 'by key'. It distinguishes itself from siblings '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?

The description provides explicit scenarios for use: stale context, task completion, clearing sensitive data. It does not directly state when not to use, but the guidance is sufficient.

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 no additional behavioral context (e.g., error handling, pagination). It does not contradict 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 very short (four words) and front-loaded. It is concise, though bordering on under-specification. For a simple retrieval tool, it is acceptable.

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

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

Given the low complexity (1 param, no output schema), the minimal description is partially complete. It identifies the key resource and parameter, but lacks information about return format or potential errors. Could be improved with a brief note on response structure.

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

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

Schema description coverage is 0%, meaning the param 'lens_id' has no description. The tool description only says 'by lens_id' without explaining what lens_id represents, its format, or providing an example. This offers minimal added meaning 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?

The description clearly states the tool retrieves a single patent by lens_id, using a specific verb and resource. It distinguishes itself from sibling 'patents_search' which implies searching, so the purpose is specific.

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

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

The description implies use when you have a specific lens_id, but does not explicitly state when not to use or mention alternatives like 'patents_search'. It provides no exclusion 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?

Description adds no behavioral traits beyond the annotations (readOnlyHint, openWorldHint). The agent gains no insight into pagination, rate limits, or result details.

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

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

At two words, the description is under-specified. Conciseness should not sacrifice necessary information; here it is insufficient.

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 good annotations, the description is extremely minimal, omitting return value, search scope, and any limitation. Not adequate for a 3-parameter tool.

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

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

With 67% schema coverage, description could add context for the undocumented 'from' parameter, but it does not. It provides no parameter-level information 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 is 'Patent search.' which is a tautology of the tool name, providing no additional specificity. It does not distinguish from sibling tools like 'patent' or 'scholarly_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 guidance on when to use this tool versus alternatives, nor any prerequisites or exclusions 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?

The description discloses rate limits (5 per identifier per day), that it's free, and that it doesn't count against the tool-call quota. No annotations contradict; the readOnlyHint=false is consistent with a feedback submission action. This adds valuable behavioral 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?

The description is a single paragraph of about 100 words, front-loading the purpose and then covering usage, constraints, and writing tips. Every sentence adds value without repetition or fluff.

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

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

For a simple feedback submission tool, the description is comprehensive: it covers what the tool does, when to use it, how to structure the input (including the optional context object), and constraints (rate limit, quota). No output schema exists, but the return is straightforward feedback confirmation.

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 meaning beyond the schema: it explains the purpose of each type value, gives examples (bug, feature, data_gap, praise), and provides writing guidelines (be specific, 1-2 sentences, 2000 chars max). This helps the agent craft proper feedback.

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 is for giving feedback to the Pipeworx team about bugs, missing features, data gaps, or praise. It specifies when to use it with concrete examples, and it distinguishes from sibling tools which are for data retrieval or analysis, not feedback.

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

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

The description explicitly tells when to use the tool (for bug reports, feature requests, etc.) and provides guidance on how to frame the feedback (describe in terms of tools/packs, don't paste user prompts). It does not explicitly state when not to use it, but the context and sibling tools make it 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 show read-only and safe. Description adds 'walks child markets', 'searches across events', 'groups and checks monotonicity', and 'returns ranked opportunities with reasoning', providing meaningful behavioral detail 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?

Single paragraph, front-loaded with purpose. Information dense but not overly verbose; could be slightly more concise but remains 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?

Covers inputs, behavior, and outputs ('returns ranked opportunities with suggested trade direction + reasoning'). No output schema, but description compensates. Could mention edge cases like no opportunities found.

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?

100% schema coverage with descriptions, but description adds significant value: explains the two modes with examples and rationale, which clarifies input semantics 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?

Clearly states it finds arbitrage opportunities via monotonicity violations, specifies two distinct modes (event and topic), and differentiates from sibling tool 'polymarket_edges' by explaining cross-event capability.

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 describes when to use each mode (event slug vs topic/seed question) and why cross-event catches cases single-event misses. Lacks explicit 'when not to use' but context 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=true and destructiveHint=false, so the agent knows it is safe. The description adds valuable behavioral context: it uses a lognormal model from FRED and live coinpaprika price, groups by asset, fetches price history once, and ranks by edge. 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 of about 80 words, front-loaded with purpose. It is concise but somewhat dense; no wasted sentences, but the structure could be improved with bullet points for 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?

Given the tool's complexity (multiple steps, model, ranking), the description covers the main workflow and output. No output schema exists, but the description specifies 'returns top N ranked by edge magnitude with suggested trade direction', which is sufficient. Annotations complement with safety info.

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

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

Schema description coverage is 100%, so each parameter is already well-documented with defaults and ranges. The description does not add new meaning beyond what the schema provides, only reiterates defaults. Baseline 3 is appropriate.

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

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

The description clearly states the verb 'scan' and 'return', the resource 'highest-volume Polymarket markets', and the output 'ones where Pipeworx data disagrees most with the market price'. It also specifies the model and ranking, distinguishing it from sibling tools like 'polymarket_arbitrage'.

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 it is 'built for the what should I bet on today question' and helps discover opportunities without manual paging. It provides clear context for when to use, though it does not explicitly exclude alternatives or mention when not to use.

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

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

Annotations provide readOnlyHint and destructiveHint; description adds scoping details (by identifier) and the ability to list keys, which is beyond structured data.

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

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

Two compact sentences with no wasted words, front-loaded purpose, and logical flow.

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

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

For a simple tool with one optional param and no output schema, description covers purpose, usage, scope, and sibling relationship fully.

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% coverage for the single parameter; description reinforces usage but adds no new format or constraints, meeting 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?

Description clearly states verb 'retrieve' or 'list' and resource 'saved values/keys', and distinguishes from siblings by naming '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 tells when to use (look up context stored earlier) and how to pair with 'remember' and 'forget', leaving no ambiguity.

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

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

Annotations already indicate readOnly and non-destructive behavior. The description adds behavioral context: parallel fan-out to three sources, return format (structured changes + count + URIs), and accepts relative date shorthands. 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 two sentences long, each sentence adds distinct value. It is front-loaded with the core purpose and example queries, then details the fan-out and return format. No superfluous text.

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

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

Despite no output schema, the description fully explains the return type (structured changes, total_changes count, citation URIs). It covers all parameters and the parallel architecture. Complete for a simple tool with no nested objects.

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

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

Schema description coverage is 100%, so parameters are already documented. The description adds practical usage tips (e.g., 'Use "30d" or "1m" for typical monitoring') and explains that 'value' accepts ticker or CIK. This adds moderate value beyond the schema.

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

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

The description clearly states the tool's purpose: answering 'What's new with a company in the last N days/months?' with specific example queries. It distinguishes from siblings by listing the data sources (SEC EDGAR, GDELT, USPTO) and the parallel fan-out approach.

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 includes explicit user queries and usage contexts (e.g., 'brief me on what happened'). It advises typical values for the 'since' parameter ('30d' or '1m'). However, it does not specify when not to use it, 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?

Description discloses key behavioral traits: key-value store, scoped by identifier, persistence duration (24h for anonymous, persistent for authenticated users), and that it avoids re-lookup. 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?

Concise 66-word description, front-loaded with purpose, then usage, then behavior. Every sentence adds value with no redundancy.

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

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

Complete for a simple memory tool: covers purpose, usage, behavior, persistence, and sibling tools. No output schema needed.

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

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

Schema already covers 100% of parameters with descriptions; description reinforces the key-value pair concept but adds no new meaning beyond schema.

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

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

Description explicitly states the tool saves data for later reuse, with specific examples like ticker, address, preference, and 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?

Provides clear guidance on when to use (discover something worth carrying forward) and explicitly pairs with recall and forget, but does not specify cases where it should not be used.

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 (readOnlyHint=true, destructiveHint=false) indicate safe read operation. The description adds value by stating it returns 'IDs plus pipeworx:// citation URIs' and mentions that it 'Replaces 2–3 lookup calls,' providing behavioral context 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 a single paragraph of moderate length, front-loading the main purpose and then providing usage guidance and examples. Every sentence adds value; there is no redundancy or 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 adequately explains return values: 'Returns IDs plus pipeworx:// citation URIs' and details the ID systems (CIK, ticker, RxCUI, LEI). It covers the two entity types and provides examples, but could be more precise about the return structure.

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

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

Input schema has 100% description coverage: both parameters have clear descriptions (type enum with values, value with examples). The tool description reinforces with examples but does not add significant new semantic meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Look up the canonical/official identifier for a company or drug.' It specifies the verb (look up), resource (identifiers), and provides concrete examples (Apple → AAPL, Ozempic → RxCUI). It also distinguishes from sibling tools by noting it replaces 2-3 lookup calls and should be used before other tools needing IDs.

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

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

The description explicitly tells when to use: 'Use when a user mentions a name and you need the CIK, ticker, RxCUI, or LEI' and 'Use this BEFORE calling other tools that need official identifiers.' This provides clear context and sequencing guidance, effectively differentiating from 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 (readOnlyHint, openWorldHint, destructiveHint) already indicate read-only, non-destructive behavior. The description adds no additional behavioral context, such as what the tool returns or how 'open world' applies.

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?

Extremely concise (3 words) but at the expense of completeness. It fails to provide needed context, making it ineffective rather than efficient.

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

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

For a tool with one required parameter and no output schema, the description is critically incomplete. It omits the tool's action, parameter semantics, and return value, leaving the agent without sufficient information to use it correctly.

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

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

Schema description coverage is 0%, and the description does not explain the 'lens_id' parameter. The agent gets no information about what the parameter means or how to use it.

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 'Single scholarly work' is vague and does not specify an action (e.g., retrieve, get). It loosely relates to the name but lacks clarity on what the tool does. A tautology with no verb.

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

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

No guidance on when to use this tool versus siblings like 'scholarly_search'. The description provides no context about appropriate use cases or 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?

The description adds no behavioral details beyond what annotations already communicate (readOnlyHint, openWorldHint). It does not mention result format, pagination, or any constraints, offering zero additional 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?

While brief, the description is under-specified; it lacks essential information. True conciseness would preserve necessary details without excess, but here the brevity sacrifices clarity.

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 three parameters, no output schema, and zero parameter documentation, the description is critically incomplete. The agent cannot determine how to invoke the tool effectively or what results to expect.

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

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

With 0% schema description coverage, the description must compensate but fails entirely. It does not explain the meaning or usage of 'from', 'size', or 'query', leaving the agent without guidance on how to populate these parameters.

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 'Scholarly works search' clearly conveys a verb ('search') and a resource ('scholarly works'), making the core purpose evident. However, it lacks differentiation from sibling tools like 'scholarly' or 'patents_search', which could cause confusion.

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

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

The description provides no guidance on when to use this tool versus alternatives, nor any conditions or prerequisites. It simply states the action without context.

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

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

Annotations already indicate readOnly and openWorld. Description adds valuable detail: returns verdict with types, extracted structured form, actual value with citations, and percent delta. 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?

Concise: 5 sentences covering purpose, usage, scope, output, and efficiency. Front-loaded with key verbs. No redundancy or filler.

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

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

For a 1-param tool without output schema, the description fully explains behavior: what claims it handles, what output to expect (verdict with details), and the data source. Complete enough for agent decision-making.

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

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

Schema coverage is 100% for the single parameter, but description adds meaning by specifying the type of claims (natural-language, company-financial) and gives examples. Also describes the output structure, compensating for lack of output schema in context.

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

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

Clearly states the tool validates factual claims, using multiple strong verbs and specifying the domain (company-financial). Distinguishes from siblings like 'scholarly' or 'bet_research' by its specific fact-checking function.

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 ('check whether something a user said is true') with examples. Also states the supported scope (company-financial claims) and notes it replaces 4-6 calls, indicating efficiency. Lacks explicit 'when not to use' but the scope restriction serves as implicit guidance.

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