Could Have Been Email

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

No annotations provided, so the description carries the full burden. It fully discloses behavior: the tool selects the best source, fills arguments, and returns results. It also sets expectations that no tool browsing or schema knowledge is needed, which is critical for an AI agent.

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

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

The description is concise at three sentences, front-loading the purpose and usage, and includes examples. Every sentence adds value without redundancy.

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

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

Given the tool's simplicity (one parameter, no output schema, no nested objects) and the lack of annotations, the description is complete. It explains input, behavior, and output expectations sufficiently for an AI agent to select and invoke the tool correctly.

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

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

Schema coverage is 100% for the single parameter 'question', which is described as 'Your question or request in natural language'. The description adds value by explaining the parameter's purpose in context (plain English, no need to learn schemas) and providing examples, going beyond the schema's minimal 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 that the tool accepts a plain English question and returns an answer from the best data source, distinguishing it from other tools by emphasizing that it eliminates the need to browse or learn schemas. Examples clarify the scope.

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 the agent to use this tool when the user asks a question in natural language, contrasting with browsing tools or learning schemas. It does not explicitly mention when not to use it or provide alternatives, but the context and examples make usage 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 (readOnlyHint, openWorldHint, destructiveHint) are present and consistent. The description adds substantial behavioral context: it explains the fan-out to different data packs depending on the bet classification, and describes the output as an 'evidence packet plus a simple market-vs-model comparison'. 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 front-loaded with the core purpose in the first sentence. It is comprehensive but each sentence adds value: it explains the input flexibility, the classification, the fan-out logic, and the output. No redundant or trivial statements.

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 what is returned (evidence packet and market-vs-model comparison). It covers all key aspects: input types, resolution, classification, data pack selection, and the comparative output. The tool's role as a central demo product is clear, leaving minimal gaps for an agent.

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

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

Input schema has 100% description coverage for both parameters. The description adds meaning beyond the schema by explaining the market parameter accepts slug, URL, or question text, and that depth 'quick' uses 2-3 sources while 'thorough' does full fan-out. This clarifies the enum options effectively.

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 action ('Research a Polymarket bet by pulling the relevant Pipeworx data'), identifies the resource (Polymarket bet), and outlines the process (resolve, classify, fan out to packs, return evidence and comparison). It distinguishes itself from siblings as the 'core demo product' that provides comprehensive bet context in one call.

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 use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It also notes that agents that get bet-relevant context here convert better than having to discover packs themselves, implying this tool is preferred for betting research over general data retrieval 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?

Describes data sources (SEC EDGAR, FDA) and output (paired data + URIs). No annotations present, so description carries full burden. Does not explicitly state non-destructive nature or error behavior, but read-only implied.

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

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

Three efficient sentences: first defines core action, second details per-type behavior, third highlights efficiency. No redundant words.

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

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

Covers input format, data sources, output type, and use case. Lacks details on error handling or rate limits, but sufficient for a simple comparison tool with no output schema.

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?

Description adds value beyond schema: explains mapping of values (tickers/CIKs vs drug names) and what data each type returns. Schema already has 100% coverage, but description enriches with concrete examples.

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 compares 2–5 entities side by side, with specific data sources for company and drug types. Distinguishes from siblings like resolve_entity or ask_pipeworx by focusing on multi-entity comparison.

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 context for when to use (e.g., replacing 8–15 sequential calls) and specifies valid entity types. 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?

With no annotations, the description carries full burden and clearly states the return values (filler word count, decisions, action items, suggested email). It does not mention any side effects or limitations, but as an analysis tool, the behavior is well-understood.

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 sentence that conveys all essential information without waste. It is front-loaded with the main purpose and concisely lists outputs.

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 and the presence of an output schema (as per context), the description is complete. It explains what the tool does and what it returns, meeting the needs for selecting and invoking 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 adds no additional meaning beyond listing some outputs; it does not elaborate on parameter usage or format. 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: checking if a meeting transcript could have been an email, and specifies the analysis outputs (filler word count, decisions, action items, suggested email). It is a specific verb-resource combination and distinguishes well from sibling tools.

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

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

The description does not provide explicit guidance on when to use this tool versus alternatives. It implies use when analyzing meeting transcripts, but lacks context such as prerequisites, when not to use it, or comparison with sibling tools.

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

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

No annotations exist, so the description carries the full burden. It explains the search function and that it returns 'the most relevant tools with names and descriptions'. It could mention that it does not modify data or have side effects, but the non-destructive nature is implied by the description. Score 4 as it provides good transparency beyond minimal.

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

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

Three sentences with zero waste. The first sentence states purpose, second describes output, third gives usage guidance. Every sentence is essential and front-loaded.

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

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

Given the tool's simplicity (2 params, no output schema), the description is complete. It covers purpose, input semantics, and when to use. No output schema means the description must describe return, which it does ('Returns the most relevant tools with names and descriptions').

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

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

Schema coverage is 100%, so the description adds value by explaining the usage pattern (natural language description). It provides examples for the query parameter and clarifies the limit parameter's default and max. This enriches the schema's own descriptions.

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

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

The description clearly states the action ('Search the Pipeworx tool catalog'), the resource ('tool catalog'), and the method ('by describing what you need'). It explicitly distinguishes itself as a tool discovery mechanism to be used before selecting 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?

The description provides explicit guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This tells the agent when to use it (first step in tool selection) and why (narrow down from many tools).

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

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

No annotations provided, so description carries burden. It discloses bundling of multiple data sources and return of citation URIs. Does not explicitly state read-only, but implies safe retrieval. No contradiction with annotations.

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

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

Two concise sentences. First sentence front-loads purpose and data sources. Second sentence adds efficiency claim and exclusion guidance. 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?

Lists data sources and return format hint (pipeworx:// URIs). No output schema, so description partially compensates. Could detail response structure more, but adequate for agent to understand breadth.

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%, baseline 3. Description adds extra meaning: clarifies value accepts ticker or CIK, and explicitly says names not supported (use resolve_entity). Adds practical guidance 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?

Clear statement of what it does: 'Full profile of an entity across every relevant Pipeworx pack in one call.' Lists specific data sources (SEC, XBRL, patents, news, LEI) and distinguishes from sequential calls and federal contract case.

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 (for full entity profile) and when not (for federal contracts, use usa_recipient_profile). Also hints at prerequisite: if only name, use resolve_entity first.

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

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

No annotations are provided, so the description carries full burden. It states deletion but does not mention side effects, whether deletion is reversible, or any authorization requirements.

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 single sentence, front-loaded with the action and resource. No wasted words.

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

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

For a simple single-parameter tool, the description is adequate but lacks behavioral context. Could mention return value or 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 description coverage is 100% (the only parameter has a description). The tool description does not add further meaning 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 'Delete a stored memory by key' clearly states the verb (delete), resource (stored memory), and means (by key). It distinguishes itself from sibling tools like 'remember' (store) and 'recall' (retrieve).

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. For example, it does not clarify whether deletion is permanent or if there are conditions that prevent deletion.

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

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

With no annotations, the description carries full burden. It discloses the rate limit (5 per day) and the requirement to be specific about tools/data. No destructive behavior is implied, and the tool is described as 'Free'.

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

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

The description is extremely concise: two sentences plus a rate-limit note. Every sentence adds value, and the purpose is stated immediately.

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 and three parameters (including nested objects), the description covers purpose, usage guidelines, rate limiting, and content quality. It is complete enough for a feedback tool, though return behavior is not mentioned.

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

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

Schema coverage is 100%, and the description adds minimal additional parameter information beyond the schema. The content guidelines for 'message' are partially redundant with the schema description. Baseline of 3 is appropriate.

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

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

The description clearly states 'Send feedback to the Pipeworx team' and enumerates specific use cases (bug reports, feature requests, missing data, praise). It distinguishes itself from sibling tools that are query or memory tools.

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

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

The description specifies when to use (feedback scenarios) and provides content guidelines (do not include end-user prompt verbatim). It also mentions rate limiting. However, it does not explicitly state when not to use alternatives, but context is clear.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds valuable context: it walks child markets, extracts dates/thresholds, sorts, and returns violation pairs with suggested trades. 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 three sentences, each earning its place: first states the core purpose, second explains the monotonicity principle, third tells how to use and what to expect. No wasted words.

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

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

For a single-parameter tool with no output schema, the description comprehensively explains the arbitrage logic, input format, and output structure. The agent has enough context 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?

With 100% schema coverage and one parameter, the description adds meaning beyond the schema by explaining the parameter as a slug or URL and how it drives the event analysis.

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 concrete examples (e.g., 'P("BTC hit $150k by May 31") ≤ P("BTC hit $150k by Jun 30")'). It distinguishes itself from siblings like polymarket_edges by focusing on within-event price ordering checks.

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

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

It tells the agent to pass a Polymarket event slug or URL and explains the logic for when to use it. However, it does not explicitly mention when not to use it or compare with alternatives like polymarket_edges.

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, openWorldHint=true, destructiveHint=false) are consistent. The description adds significant behavioral detail: it scans top markets, groups by asset, fetches price history once, computes model probability, and ranks by |edge|, with suggested trade direction. This goes well 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 well-structured paragraph that front-loads the purpose, explains the mechanics in a few sentences, and concludes with output and use case. 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?

Despite lacking an output schema, the description fully explains the return value (top N ranked by edge magnitude with suggested trade direction) and covers inputs (limit, window, min_edge_pp). The tool's internal model (lognormal from FRED, coinpaprika) is described, making the behavior understandable.

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

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

Schema coverage is 100% with clear parameter descriptions. The tool description adds context by explaining the grouping and fetching logic, which clarifies the purpose of the 'window' parameter (volume filter). This adds meaning beyond the schema's individual 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 verb 'scan' and the resource 'highest-volume Polymarket markets', with a specific purpose: returning markets where Pipeworx data disagrees most with market price. It distinguishes from the sibling 'polymarket_arbitrage' by focusing on edge detection rather than arbitrage execution.

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 targets the 'what should I bet on today' use case, mentioning discovery without manual paging. However, it does not state when not to use the tool or name alternative tools (e.g., 'ask_pipeworx' or 'bet_research') for different queries.

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

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

No annotations are provided, so the description carries full burden. It discloses that omitting key lists all memories, implying a read-only operation. It doesn't mention side effects or persistence details, but the tool is inherently non-destructive. 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?

Two sentences, clear and front-loaded: first sentence states core action and condition, second provides usage context. No 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?

Given no output schema and simple parameter, description is sufficient. It explains the two modes and the purpose. Could mention return format (e.g., returns the stored value or list of keys) but not essential.

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% (1 parameter fully described). The description adds context that omitting the key lists all memories, which is not in the schema description. This adds value beyond the schema.

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

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

Description clearly states the tool retrieves a memory by key or lists all memories. The verb 'retrieve' and resource 'memory' are specific, and the dual behavior (by key vs list all) is explicitly described. It distinguishes from sibling 'remember' (store) and 'forget' (delete).

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: 'to retrieve context you saved earlier'. It contrasts with alternatives implicitly by mentioning the dual behavior (by key or omit for list), and the sibling tool names suggest 'remember' is for storing, 'forget' for deleting, making the usage clear.

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

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

With no annotations, the description fully discloses the fan-out behavior (parallel queries to three sources), accepted date formats, and return structure (structured changes, count, URIs). It does not mention authentication or error handling, but the behavior is sufficiently transparent for safe invocation.

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

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

The description is a single dense paragraph that front-loads the purpose and then efficiently covers behavior, parameters, and return format. Every sentence adds value, with 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 tool with three required parameters and no output schema, the description covers purpose, behavior, accepted inputs, and return structure. It lacks error handling details, but the provided information is sufficient for typical usage scenarios.

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% parameter descriptions, and the description adds substantial context: the fan-out logic, accepted date formats, and distinction between ticker and CIK. This goes well beyond the schema's minimal descriptions, providing critical operational understanding.

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

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

The description clearly states the tool's purpose: 'What's new about an entity since a given point in time.' It specifies the entity type (company) and the sources used (SEC EDGAR, GDELT, USPTO), making the scope precise and differentiating from generic change-tracking tools.

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

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

The description explicitly recommends usage for 'brief me on what happened with X' or change-monitoring workflows. It does not provide when-not-to-use guidance or compare to siblings, but the context is clear enough for an AI agent to select this tool for temporal change queries.

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?

Since no annotations are provided, the description carries the full burden. It discloses behavioral traits: persistence based on authentication status (authenticated gets persistent, anonymous lasts 24 hours). This adds useful context about memory lifetime, though it does not detail overwrite behavior or storage limits.

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, then usage guidance and behavioral note. 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?

Given the simple tool (2 params, no output schema, no nested objects), the description is largely complete. It covers purpose, usage, and persistence behavior. However, it could mention if storing the same key overwrites or appends, which is a minor gap.

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

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

Schema coverage is 100%, so the baseline is 3. The description mentions key-value pair storage but does not add significant meaning beyond what the input schema provides (key and value descriptions with examples). The examples in schema are already clear.

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 stores a key-value pair in session memory, specifying the verb 'store' and the resource 'key-value pair'. It distinguishes from siblings like 'recall' (retrieve) and 'forget' (delete) by focusing on storage.

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 examples of when to use this tool ('save intermediate findings, user preferences, or context across tool calls') and notes persistence differences for authenticated vs anonymous users. However, it does not explicitly mention when not to use it or alternatives beyond the sibling tools.

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

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

No annotations are provided, so the description carries full burden. It describes a read-only lookup (resolve and return) but does not explicitly state it has no side effects, required permissions, or any other behavioral traits. Adequate but not thorough.

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 with no wasted words. It efficiently covers purpose, version, supported type, input examples, and expected output.

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

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

Given the simple input schema and no output schema, the description adequately describes the return values (ticker, CIK, name, URIs). It also mentions the tool replaces multiple calls, providing context. Almost complete for this 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?

Both parameters (type and value) have schema descriptions (100% coverage). The description adds examples and clarifies acceptable formats for value (ticker, CIK, name), which enriches understanding beyond the schema 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?

The description clearly states the tool resolves entities to canonical IDs, specifies the supported type ('company'), and provides concrete examples of inputs and outputs. This distinguishes it from sibling tools like ask_pipeworx (likely a query tool) and memory tools.

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

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

The description explicitly says it replaces 2–3 lookup calls, implying the use case of consolidating multiple lookups. However, it does not explicitly state when not to use it or provide alternatives, but the 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?

No annotations are provided, so the description carries full burden. It discloses the output (verdict, structured form, actual value with citation, percent delta) and that it is read-only in nature. However, it does not explicitly confirm safety (e.g., no side effects) or address potential issues like rate limits or authentication.

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

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

Two efficient sentences: first states purpose and domain, second lists output details and benefits. No wasted words, front-loaded with the core function.

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

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

Given a simple single-parameter tool without output schema or annotations, the description covers purpose, domain, output, and value proposition. It could mention the limitation to US public companies more explicitly, but it is already implied by 'v1 supports company-financial claims'.

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

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

Only one parameter (claim) exists with 100% schema description coverage. The description adds examples and clarifies it is a natural-language claim, but the schema already provides adequate description. No additional semantic depth 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 fact-checks natural-language claims against authoritative sources, specifying the domain (company-financial claims for US public companies) and listing the verdict types. It distinguishes itself from siblings like compare_entities or entity_profile by focusing on factual verification.

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

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

The description explains when to use the tool (for fact-checking claims, especially financial) and what it replaces (sequential agent calls). It implies limitations to company-financial claims but does not explicitly state when not to use it or provide direct alternatives.

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