String Db

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

Annotations already indicate read-only, open-world, non-destructive. The description adds that it routes to 1,423+ tools and returns structured answers with stable citation URIs, providing 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 well-organized with examples and a clear recommendation, but it is somewhat verbose and could be trimmed 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?

For a tool with one parameter and no output schema, the description gives sufficient context about the broad scope and output format (structured with citations), though more detail on response structure would improve completeness.

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

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

Schema description coverage is 100% for the single parameter 'question', and the description adds minimal new meaning beyond 'Your question in natural language' already in the schema. Baseline score fits.

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

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

The description clearly states it answers factual questions about real-world data with structured answers and citations, explicitly distinguishing it from web search and sibling tools by listing domains and use cases.

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

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

The description explicitly says to prefer over web search and gives lists of valid queries and examples, but does not explicitly state when not to use it, though the context of sibling tools implies boundaries.

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

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

Annotations already indicate readOnly, openWorld, non-destructive. Description adds: resolves market, classifies bet, fans out to relevant packs, returns evidence packet + market-vs-model comparison. 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?

Four dense sentences, front-loaded with core action. Slightly verbose in places (e.g., parenthetical examples) but no wasted words overall.

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

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

No output schema, but description details input, fan-out logic, return type (evidence packet + comparison). Covers complexity well: resolves market, classifies, fans out to packs, returns comparison.

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

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

Schema coverage 100%, description enriches: explains market param accepts slug, URL, or question text with examples; depth param has quick/thorough with default. Adds meaning beyond enum and type.

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 states specific verb 'research', resource 'Polymarket bet', and scope 'pulling Pipeworx data'. Clearly distinguishes from siblings by noting it is the core demo product that automates fan-out, avoiding need to discover packs manually.

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: 'should I bet on X?', 'what does the data say...', 'is there edge...?' and contrasts with alternatives: 'agents that get bet-relevant context here convert better than ones that have to discover the packs themselves'.

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, openWorldHint=true, destructiveHint=false, which are consistent. Description adds context: data sources (SEC EDGAR/XBRL, FAERS, FDA), return of paired data + citation URIs, and efficiency over sequential calls. No contradictions.

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

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

Single paragraph of ~80 words, front-loaded with main purpose, then use cases, then type details. Every sentence is informative with no redundancy. Concise and 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?

No output schema, but description explains return of paired data + citation URIs and specifics for each type. Could be more detailed about output structure, but sufficient for agent understanding given the examples.

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 both parameters are described. The description adds meaning: explains type options and what data each pulls, gives examples for values (tickers vs drug names), and reinforces min/max items. This adds value beyond the schema's descriptions.

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

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

The description clearly states the tool compares 2-5 companies or drugs side by side, with specific data types for each. It uses a specific verb ('compare') and resource ('entities' as companies/drugs), and distinguishes 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?

Explicit use cases are given (e.g., 'compare X and Y', 'X vs Y'). It implies when to use (comparisons, rankings) but lacks explicit when-not or alternative sibling tool names. The description is clear about the context for usage.

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

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

Annotations already indicate readOnlyHint=true (safe read operation). The description adds that it returns the top-N most relevant tools with names and descriptions, which is useful context beyond the annotation. No destructive behavior mentioned, consistent 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 front-loaded with the main action ('Find tools') and includes necessary examples and usage guidance. While the list of domains is somewhat lengthy, each example adds value for the agent. Overall efficient and 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?

For a simple search/discovery tool, the description covers purpose, usage, parameters, and return format. It lacks details on error handling or behavior when no tools match, but given the simplicity and annotations, it is sufficiently complete.

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

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

Schema coverage is 100%, so baseline is 3. The description reinforces the query parameter (natural language) and limit (top-N) but does not add new semantic information beyond what the schema already provides.

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

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

The description clearly states the tool's purpose as finding tools by describing a data or task, which distinguishes it from sibling tools like ask_pipeworx or compare_entities. It provides specific examples of domains (SEC filings, financials, etc.) and explicitly says to call it first, reinforcing its unique 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?

The description explicitly states when to use the tool ('when you need to browse, search, look up, or discover what tools exist for') and provides guidance to call it first when many tools are available, implying not to use it when you already know the desired tool.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds minimal behavioral context. It does not describe return format, side effects, or authorization needs, but 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?

Single sentence with no wasted words. It front-loads the core function and effectively communicates the tool's purpose.

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

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

Despite lacking output schema and having 0% schema coverage, the description offers no additional context. It fails to explain expected outputs, parameter roles, or usage context, making it inadequate for a complex bioinformatics tool.

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

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

Schema description coverage is 0%, yet the description provides no information about parameters. It does not explain the 'species' number or the format of 'identifiers', leaving the agent to infer from names alone.

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

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

Description clearly states it performs functional enrichment for a gene set, listing specific databases (GO, KEGG, Pfam, Reactome). It uses a specific verb ('enrichment') and resource ('gene set'), and the purpose is distinct from sibling tools like homology or interactions.

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. The description does not mention when enrichment is appropriate or what inputs are needed beyond the schema.

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 read-only and non-destructive. Description adds the specific data returned (SEC filings, financials, patents, news, LEI) and citation format, providing valuable behavioral context.

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

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

Single, well-structured paragraph. Purpose front-loaded, followed by usage examples and return details. No redundant sentences.

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 aggregator tool without output schema, listing return categories (filings, fundamentals, patents, news, LEI) is thorough. Could specify recency or period details, but sufficient for agent to invoke.

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

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

Schema covers both parameters with descriptions. Description adds extra context: clarifies type only accepts 'company' and value must be ticker or CIK, not names, supplementing 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 'Get everything about a company in one call' with specific verbs and resources. Distinguishes from siblings by noting it avoids multiple pack tool calls. Provides example user queries.

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

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

Explicitly states when to use (queries like 'tell me about X') and when not to (names not supported, advising use of resolve_entity first). Offers 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 mark destructiveHint=true. The description confirms this by saying 'delete a previously stored memory', but does not add further behavioral details beyond what annotations imply. 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?

The description is very concise: one functional sentence and a usage guideline sentence, both front-loaded with no wasted words.

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

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

Given a simple one-parameter tool with no output schema, the description covers purpose, usage, and relation to siblings 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% with a clear description for the 'key' parameter. The description does not add new information beyond what the schema already provides, so baseline 3 is appropriate.

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

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

The description clearly states the verb 'delete' and the resource 'previously stored memory', specifying the key parameter. It also distinguishes itself from sibling tools 'remember' and 'recall' by implying deletion vs. storage or retrieval.

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

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

The description explicitly provides scenarios for use: 'when context is stale, the task is done, or you want to clear sensitive data', and pairs with 'remember and recall', offering clear guidance on when and with which tools 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, openWorldHint, and destructiveHint. Description adds no behavioral context beyond these, missing an opportunity to explain open-world semantics.

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 short, but lacks structured information. Under-specification, not efficient communication.

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

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

No output schema and no explanation of what the tool returns. For a bioinformatics homology tool, this is severely incomplete.

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 0% description coverage; description 'Homology mappings' adds no meaning to 'species' or 'identifiers' parameters, which need explanation 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?

Description says 'Homology mappings' which is vague. It states a topic but not a clear verb or resource, and does not distinguish from sibling tools like 'enrichment' or 'network'.

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

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

No guidance on when to use this tool versus alternatives. The description offers no context for selection.

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

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

Annotations (readOnlyHint, openWorldHint) cover safety and scope, but the description adds no behavioral traits (e.g., pagination, rate limits, result aggregation). For a tool with no output schema, the description should compensate.

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. It is front-loaded with the key resource and context, though could be more 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?

With 5 parameters, no output schema, and a minimal description, the tool lacks essential context such as return format, expected input formats, or behavior for edge cases (e.g., multiple identifiers). Very incomplete.

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 60%; parameters 'species' and 'identifiers' lack descriptions. The tool description does not elaborate on any parameter meaning. It adds no 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 indicates the resource (interaction partners) and context (for a set of proteins), but lacks an explicit verb like 'retrieve' or 'get'. It distinguishes from sibling tools like 'network' by specifying proteins, though not strongly.

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 context cues. The description is a bare noun phrase with no actionable usage information.

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

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

Annotations indicate readOnlyHint=true and destructiveHint=false, but the description adds no behavioral context beyond that. It does not mention side effects, return characteristics, or any constraints, so it barely contributes 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 extremely short, but it is not effectively concise; it omits critical information. Brevity is only valuable when it still conveys essential meaning, which it fails to do here.

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, minimal annotations, and four undocumented parameters, the description is severely incomplete. The agent cannot reliably select or invoke this tool based on the provided information.

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 and four parameters (species, identifiers, network_type, required_score), the description does not explain any parameter's purpose or format. The agent has no guidance on how to fill the required 'identifiers' or optional fields.

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 'Network image url + tabular interaction data' is vague; it does not specify an action (e.g., fetch, create, update) or clearly define what the tool does. The resource is implied but the verb is missing, making it ambiguous.

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

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

No guidance is provided on when to use this tool versus its many siblings (e.g., interactions, homology, enrich). There is no mention of context, prerequisites, or alternatives, leaving the agent without decision support.

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 rate limit (5 per identifier per day) and that it's free and doesn't count against tool-call quota. No contradiction with 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?

Description is dense but front-loaded with purpose. Could benefit from bullet points for readability, but every sentence adds value without redundancy.

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

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

Covers all necessary context: what feedback types, how to write, limitations, and team response. No output schema, but return value is implied. Complete for a feedback tool.

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

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

Adds meaning beyond schema: explains each enum value in detail, provides guidance on message length (1-2 sentences, 2000 chars) and specificity. Schema coverage is 100%, but description enriches semantics.

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 sends feedback (bug, feature, data_gap, praise) to the Pipeworx team. Specific verb 'Tell' and resource 'Pipeworx team' with enumerated feedback types, distinguishing it from sibling tools like 'ask_pipeworx' or 'discover_tools'.

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

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

Explicitly when to use: 'when a tool returns wrong/stale data', 'when a tool you wish existed isn't in the catalog', 'when something worked surprisingly well'. Provides do's/don'ts: 'describe issue in terms of Pipeworx tools/packs', 'don't paste end-user prompt'.

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 no destructive action; the description adds non-contradictory details about walking child markets, extracting dates/thresholds, and sorting, enhancing 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 somewhat lengthy but every sentence contributes value, explaining the concept, mechanism, and output format. Could be slightly more concise 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?

Given a simple single-parameter schema and no output schema, the description fully explains the tool's purpose, logic, and return format, making it self-contained for an agent to use correctly.

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

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

Schema coverage is 100% with a clear description of the 'event' parameter. The description adds context by explaining the expected format (slug or URL) and example usage, going 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 finds arbitrage opportunities via monotonicity violations, with a concrete example distinguishing it from siblings like 'polymarket_edges'.

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

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

It explains when to use (any Polymarket event slug/URL) and the reasoning behind arbitrage, but does not explicitly mention when not to use or suggest alternative tools.

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

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

Annotations already declare readOnlyHint and openWorldHint. The description adds significant behavioral context: scanning top markets, grouping by asset, using FRED and coinpaprika data, computing model probability, ranking by edge, and suggesting direction. 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 detailed and well-structured, but slightly verbose. It front-loads the main purpose and each sentence adds value, though a more concise version would still be effective.

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

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

Given the tool's complexity, the description explains the model and process well. However, without an output schema, it could more clearly describe the return format (e.g., JSON structure). Overall, it provides sufficient context for an agent to use the tool.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description does not add new parameter-specific meaning beyond what is already in the schema.

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

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

The description uses specific verbs ('scan', 'return') and clearly identifies the resource (Polymarket markets where Pipeworx data disagrees). It distinguishes this tool from siblings like 'polymarket_arbitrage' by focusing on edge discovery.

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

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

The description explicitly states the intended use case ('what should I bet on today') and explains the tool avoids manual paging. However, it does not explicitly mention when not to use it or name alternative tools.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds behavioral context: it is scoped to an identifier, can list all keys, and pairs with remember/forget. 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?

Two sentences, front-loaded with purpose, no wasted words. Efficient and clear.

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 tool (one optional param, no output schema), the description is complete: explains both retrieval and listing modes, scope, and relationships to sibling tools.

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 the 'key' parameter fully (description explains omit to list). The description reiterates this but adds no new semantic detail. 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: 'Retrieve a value previously saved via remember, or list all saved keys'. It specifies the verb (retrieve/list), resource (saved values/keys), and differentiates from siblings like 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?

The description provides context on when to use ('Use to look up context the agent stored earlier ... without re-deriving it from scratch') and how to use ('omit the key argument' for listing). It does not explicitly state when not to use, 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?

Description adds behavioral context beyond annotations: fans out to three sources (SEC EDGAR, GDELT, USPTO), returns structured changes + count + citation URIs, and explains 'since' formats. Annotations already indicate read-only and open-world, so description adds meaningful detail without contradiction.

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

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

Description is well-structured with a clear purpose statement, usage examples, and a concise list of sources. At ~100 words it is appropriately sized, though a bit dense.

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 the main workflow (fan-out to sources), return structure (changes, count, URIs), and parameter formats. Lacks details on pagination, limits, or error handling, but given the open-world annotation and no output schema, it is adequately complete.

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

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

Schema description coverage is 100%, so baseline is 3. Description adds that 'type' only supports 'company' and provides examples for 'since' format (ISO date or relative), but these are minor enhancements over the schema descriptions.

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

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

Description clearly states 'What's new with a company in the last N days/months?' and provides multiple usage examples. It distinguishes itself from sibling tools like entity_profile and compare_entities by focusing on recent changes across SEC, GDELT, and USPTO.

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 advises use when user asks about updates or changes ('what's happening with X?', 'brief me on...') and gives concrete query patterns. Does not explicitly mention when not to use, but the specificity makes 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?

Beyond annotations (readOnlyHint=false, destructiveHint=false), the description adds key behavioral details: scoped by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. 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 concise (4 sentences) and well-structured: first sentence states purpose, second gives usage triggers, third explains scoping/persistence, fourth mentions related tools. No wasted words.

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

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

Given no output schema, the description covers what the tool does, when to use it, and retention details. It doesn't mention return values or error handling, but for a simple store operation this is sufficient.

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

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

Schema coverage is 100%, so baseline is 3. The description adds value by providing examples for key and value (e.g., 'subject_property', 'target_ticker'), enhancing understanding beyond the schema's brief descriptions.

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

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

The description clearly states the tool's purpose: 'Save data the agent will need to reuse later' with a specific verb ('Save') and resource ('data'). It distinguishes from sibling tools like recall and forget by pairing with them.

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

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

The description explicitly says 'Use when you discover something worth carrying forward' and provides concrete examples (resolved ticker, target address, etc.). It also guides the agent to pair with recall and forget, offering 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. Description only adds 'map...to STRING identifiers', which is already apparent from the tool name and schema. No additional behavioral context (e.g., rate limits, 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?

Single sentence, concise and free of clutter. Could be slightly more informative without adding length, but achieves brevity effectively.

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?

Adequate for a simple tool with 3 parameters and no output schema. Describes input and output direction, but lacks details on output format (e.g., array of strings), error handling, or limitations. With annotations covering safety, it meets minimum viability.

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 67%, with 'limit' and 'species' described. Description adds meaning to the undocumented 'identifiers' parameter by specifying 'free-text identifiers (gene symbols, accessions)', compensating for the coverage gap.

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 maps free-text identifiers (gene symbols, accessions) to STRING identifiers. The verb 'map' and specific resource 'identifiers' make the purpose unambiguous, and it distinguishes from sibling like 'resolve_entity' by specifying input type.

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

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

No guidance on when to use this tool versus alternatives like 'resolve_entity'. Implies usage for free-text identifiers but lacks explicit context, exclusions, or when-not scenarios.

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

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

Annotations already declare readOnlyHint true and destructiveHint false. The description adds value by detailing return types (IDs and citation URIs) and providing examples of outputs, going 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 concise paragraph that front-loads the purpose, includes usage guidance, examples, and explicit instruction. Every sentence adds value with no wasted words.

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

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

Despite lacking an output schema, the description compensates by listing possible IDs and citation URIs. It covers both entity types and provides usage context. Slightly incomplete due to missing exact response structure, but sufficient 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?

Input schema coverage is 100%, but the description significantly enhances understanding by explaining accepted values (ticker, CIK, name for company; brand/generic for drug) and giving examples. This goes well beyond the schema's minimal descriptions.

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

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

The description clearly states the tool looks up canonical identifiers for companies and drugs, specifying the output types (CIK, ticker, RxCUI, LEI) and giving concrete examples. It distinguishes itself from siblings by noting it replaces 2-3 lookup calls and serves as a prerequisite for other tools.

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

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

Explicitly instructs to use this tool before other tools that need official identifiers. Provides a clear when-to-use context but does not explicitly mention when not to use it or list alternatives among 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 already indicate readOnlyHint=true and destructiveHint=false. The description adds behavioral context by explaining that the tool performs multiple internal steps (NL parsing, entity resolution, data lookup, numeric comparison) and returns a structured verdict with citations. It does not fully disclose failure modes for unsupported claims.

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 (4-5 sentences) and front-loaded with the core purpose. Every sentence adds value: purpose, usage guidance, domain scope, return structure, and efficiency benefit. No redundant or verbose phrasing.

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

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

Given the tool's simplicity (one parameter, clear annotations), the description covers purpose, usage, domain, and return format. It lacks details on error handling or performance, but the output schema is not provided; the description compensates by listing return fields. Schema coverage is 100%.

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 'claim' parameter, providing examples. The tool description does not add semantic detail beyond what the schema already offers, keeping this at the baseline score.

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: fact-check, verify, validate, or confirm/refute natural-language factual claims. It specifies the domain (company-financial claims via SEC EDGAR + XBRL) and distinguishes itself from sibling tools like ask_pipeworx or entity_profile by focusing on claim validation.

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 ('when an agent needs to check whether something a user said is true') and gives example queries. It also notes the current limitation to company-financial claims. However, it does not explicitly mention when not to use it or name alternative tools 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.