openalex

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

Annotations already provide read-only, idempotent, and non-destructive hints. The description adds valuable context: default model is free, Anthropic requires BYO key and incurs costs, and the return format includes per-model details. 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 four sentences, front-loaded with the core functionality, then details options and use cases. No redundant or irrelevant information.

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

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

Despite no output schema, the description explains the return structure (per-model objects with score, confidence, signals, raw_response, plus combined view). Use cases are listed. Minor missing details on error handling, but sufficient for a probing tool.

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

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

Schema coverage is 100%, so the description's addition is incremental. It clarifies default model, the role of _apiKey (pass-through), and how context helps disambiguate. This adds meaning beyond the schema.

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

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

The description clearly states the action ('probe'), the target ('LLMs'), and the output ('score visibility 0-100 per model'). It distinguishes itself from sibling tools like 'scan_competitor_ai_presence' by focusing on specific LLM knowledge probing rather than general presence scanning.

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 optional parameters (_apiKey for Anthropic, context for disambiguation) and lists use cases. It does not explicitly state when not to use the tool, but the self-contained nature 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?

No annotations are provided, so the description carries the full burden. It discloses key behavioral traits: the tool selects the best data source automatically, fills arguments internally, and returns results. However, it lacks details on limitations (e.g., rate limits, error handling, or data freshness). The description doesn't contradict any annotations, as none exist.

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 appropriately sized and front-loaded: the first sentence states the core functionality, followed by clarifying details and examples. Every sentence earns its place by explaining the tool's value proposition, usage context, and practical applications 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 complexity (natural language processing to select data sources) and lack of annotations/output schema, the description is mostly complete. It covers purpose, usage, and behavior well, but could improve by mentioning potential limitations or response formats. The absence of an output schema means the description should ideally hint at return types, though it partially compensates with 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?

The schema description coverage is 100%, with the single parameter 'question' well-documented in the schema. The description adds minimal value beyond the schema by emphasizing 'plain English' and 'natural language,' but doesn't provide additional syntax or format details. With high schema coverage, the baseline score of 3 is appropriate.

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

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

The description clearly states the tool's purpose: 'Ask a question in plain English and get an answer from the best available data source.' It specifies the verb ('ask'), resource ('answer'), and mechanism ('Pipeworx picks the right tool, fills the arguments'). It distinguishes from siblings by emphasizing natural language input versus structured queries in other tools like search_authors or search_works.

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 this tool: 'No need to browse tools or learn schemas — just describe what you need.' It provides clear alternatives by contrasting with sibling tools that require specific parameters or schemas. The examples further illustrate appropriate use cases, such as factual queries or data lookups.

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, and the description confirms it only pulls data, not modifies. It adds behavioral detail: fan-outs to relevant packs, classifies bets, and returns a comparison. No contradiction. Could mention error handling for unresolvable markets.

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 long but each sentence adds value. It front-loads the action and purpose, then details the process and use cases. Could be slightly more concise, but overall efficient and structured well.

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

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

Without an output schema, the description effectively states what the return includes: evidence packet and market-vs-model comparison. It covers the tool's behavior, use cases, and classifications. Missing details on error responses or edge cases, but sufficient for most agents.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds meaning: explains 'quick' vs 'thorough' depth with specific behavior, and clarifies that 'market' accepts slug, URL, or question text. This adds context beyond the schema's 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?

The description clearly states the tool researches Polymarket bets by pulling Pipeworx data, resolving the market, classifying the bet, and returning an evidence packet with comparison. It uses specific verbs and resources, distinguishing it from siblings like ask_pipeworx or validate_claim.

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

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

The description explicitly says when to use: 'should I bet on X?', 'what does the data say about this Polymarket market?', or 'is there edge in this bet?'. It implies the tool is for Polymarket betting context but does not explicitly state when not to use it or mention alternative tools for other 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?

With no annotations, the description carries full burden. It clearly states the tool is read-only (retrieving data from SEC EDGAR and FDA sources) and returns paired data with URIs. It does not cover error handling or behavior for missing entities, but for a query tool this is sufficient.

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

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

Three sentences with zero waste. First sentence states core purpose, second details the two modes with specific data fields, third adds value proposition. Information is front-loaded and easy to parse.

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

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

Given the tool's simplicity (2 parameters, no output schema), the description fully covers what the tool does, what inputs it expects, and what outputs it returns (paired data + URIs). No additional information is needed for an agent to use it correctly.

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

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

Schema coverage is 100%, but the description adds significant value by explaining the two entity types with concrete examples (tickers/CIKs for company, drug names) and the constraints (2-5 items). This goes beyond the schema's plain 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 entities side by side, with specific data types for companies (revenue, net income, etc.) and drugs (adverse event counts, FDA approvals, etc.). It distinguishes from siblings by emphasizing efficiency, replacing 8-15 sequential calls, and providing unique resource URIs.

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

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

The description implies when to use the tool (batch comparison) and highlights efficiency gains, but does not explicitly state when not to use it or name alternative tools. However, the context of sibling tools (e.g., get_concept for single entities) provides implicit guidance.

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It describes the search functionality and output format, but lacks details on limitations (e.g., search accuracy, performance), authentication requirements, or error handling. The mention of '500+ tools' provides some context, but more operational details would be helpful.

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 efficiently structured in two sentences: the first states the purpose and output, the second provides crucial usage guidance. Every phrase adds value without redundancy, making it easy to parse and understand quickly.

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

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

Given the tool's moderate complexity (search functionality with 2 parameters) and 100% schema coverage but no output schema or annotations, the description is mostly complete. It covers purpose, usage context, and output format, though additional behavioral details (like search constraints or result ordering) would make it fully comprehensive.

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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema (e.g., it doesn't explain query formatting best practices or limit implications). Baseline 3 is appropriate when the schema handles parameter documentation.

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 with specific verbs ('Search the Pipeworx tool catalog') and resource ('tool catalog'), and distinguishes it from siblings by specifying it's for discovering tools rather than concepts, authors, institutions, or works. The phrase 'Returns the most relevant tools with names and descriptions' further clarifies the output.

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

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

The description provides explicit usage guidance: 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This specifies both when to use it (large catalog scenarios) and its primary role in the workflow, distinguishing it from potential alternatives like browsing or manual 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?

No annotations provided, so description carries full burden. It discloses returned data types and that results include pipeworx:// citation URIs. However, it does not discuss error conditions or what happens if the entity is not found. Still, for a read-only profile tool, this is fairly transparent.

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 concise sentences with zero waste. First sentence states purpose, second details data types, third gives alternative. Every sentence adds value.

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

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

Given the complexity of multiple data sources, the description is complete. It lists all major categories, notes output URIs, provides an alternative for federal contracts, and implies prerequisites for names. No output schema exists but return values are described.

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 description adds meaning: explains type enum (only company supported), value can be ticker or CIK, and names are not supported (recommends resolve_entity). This significantly enriches 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 'Full profile of an entity across every relevant Pipeworx pack in one call' and lists specific data types (SEC filings, financials, patents, news, LEI). It distinguishes from siblings by naming an alternative (usa_recipient_profile) and noting it replaces 10-15 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?

Explicitly states when to use (need full profile) and when not to (federal contracts → usa_recipient_profile). Also implies that for names, use resolve_entity first, providing clear context and exclusions.

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. 'Delete' implies a destructive mutation, but it doesn't disclose whether deletion is permanent, reversible, requires specific permissions, or has side effects. The description is minimal and lacks behavioral context beyond the basic action.

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, efficient sentence with zero waste. It's front-loaded with the core action and resource, making it immediately clear without unnecessary elaboration.

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 destructive tool with no annotations and no output schema, the description is inadequate. It doesn't explain what constitutes a 'stored memory', how deletion affects the system, what the response looks like, or error conditions. Given the complexity of a delete operation, more context is needed.

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

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

Schema description coverage is 100% (the 'key' parameter is fully documented in the schema), so the baseline is 3. The description adds no additional parameter semantics beyond what the schema already states ('Memory key to delete'), providing no extra value.

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

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

The description clearly states the action ('Delete') and resource ('a stored memory by key'), making the purpose immediately understandable. It doesn't explicitly differentiate from sibling tools like 'recall' or 'remember', but the verb 'Delete' strongly implies a destructive operation distinct from retrieval or creation.

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

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

The description provides no guidance on when to use this tool versus alternatives. With sibling tools like 'recall' (likely for retrieving memories) and 'remember' (likely for storing memories), there's no indication of prerequisites, when deletion is appropriate, or what happens if the key doesn't exist.

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, idempotentHint, destructiveHint) already indicate safe read-only behavior. Description adds valuable details: fetches page, extracts title/description/key links, outputs standard markdown. No contradictions and no missing behavior traits.

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

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

Description is concise (3 sentences), front-loaded with the main action, and each sentence adds value. No wasted words.

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

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

Given the tool's simplicity (2 params, no output schema, clear annotations), the description covers all necessary context: purpose, method, output format, and use cases. No gaps.

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

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

Input schema has 100% coverage with descriptions for both parameters. Description does not add new parameter-level semantics beyond what the schema provides. Baseline 3 is appropriate.

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

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

Description clearly states the verb (generate), resource (llms.txt file), and target (any URL). It specifies the output format and context (AI crawlers indexing). Among sibling tools, none are similar, so it is well-distinguished.

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

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

Description lists three explicit use cases (client site indexing, own project drafting, competitor auditing). It does not mention when not to use or alternatives, but the use cases cover common scenarios. No similar sibling exists, so exclusion is less critical.

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 provided, the description carries full burden but only states what the tool returns without disclosing behavioral traits like error handling, rate limits, authentication needs, or whether it's read-only. It mentions the return structure but doesn't explain format, pagination, or potential side effects.

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 appropriately sized with two sentences that efficiently convey purpose and return values. It's front-loaded with the core function, though the second sentence could be slightly more concise. Every sentence earns its place by adding value.

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

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

For a simple lookup tool with 1 parameter and no output schema, the description adequately covers the basic purpose and return structure. However, without annotations or output schema, it should ideally provide more behavioral context about what 'look up' entails operationally and the format of returned data.

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

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

Schema description coverage is 100% with the single parameter 'query' well-documented in the schema. The description adds no additional parameter semantics beyond what's in the schema, but doesn't need to compensate for gaps. Baseline 3 is appropriate when schema does the heavy lifting.

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 specific action ('look up'), resource ('academic concept or field of study'), and scope ('by name'). It distinguishes from sibling tools like search_authors, search_institutions, and search_works by specifying it operates on concepts rather than authors, institutions, or works.

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

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

The description implies usage when needing concept information by name, but provides no explicit guidance on when to use this versus alternatives or any exclusions. It doesn't mention prerequisites, limitations, or comparison with other concept-related tools that might exist.

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

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

The description discloses rate limiting and content expectations, but no annotations exist. It does not specify whether the action is read-only or destructive, nor does it describe the response behavior (e.g., fire-and-forget). The information provided is adequate but not comprehensive.

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 brief, front-loaded with purpose, and contains only relevant information. Every sentence serves a clear function, making it easy for an agent to parse quickly.

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 (no output schema, few parameters), the description covers the primary aspects: purpose, usage guidelines, and rate limits. It could mention whether feedback is stored or causes side effects, but it is largely complete for its complexity.

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

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

The input schema covers all parameters with descriptions, achieving 100% coverage. The description adds value by clarifying usage guidelines (e.g., message content rules) but does not significantly enhance parameter semantics beyond the schema. Baseline score of 3 is appropriate.

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

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

The description directly states the tool's purpose: 'Send feedback to the Pipeworx team.' It enumerates specific use cases (bug reports, feature requests, missing data, praise) and distinguishes itself from sibling tools, none of which are for feedback.

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

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

The tool provides clear guidance on when to use it (for various feedback types) and includes content rules (avoid including end-user's prompt verbatim) and rate limits. It does not explicitly contrast with alternatives, but no sibling tool serves a similar purpose, so the guidance is effective.

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 readOnly, openWorld, idempotent, non-destructive. Description adds caching behavior (5min-1h), data source (CF analytics-engine), and privacy (no PII), which are useful beyond annotations.

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

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

Description is slightly long but well-structured with main purpose first, then use cases, then technical notes. Every sentence adds value, no redundancy.

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

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

For a simple read-only tool with one parameter and no output schema, the description covers purpose, input semantics, behavioral details, and use cases. It lacks exact output format but the return items are listed.

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

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

Only one parameter (window) with 100% schema coverage. Description adds contextual guidance: shorter windows for hot trends, longer for steady-state demand, beyond the enum 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 returns top tools, top packs, and total call volume over a specified window. It uses specific verbs ('returns') and resource ('trending activity on Pipeworx'). It distinguishes from siblings like 'discover_tools' by focusing on current usage by AI agents.

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?

Three explicit use cases are provided (discovering hot data sources, confirming canonical tools, aligning use case with agents). No explicit exclusions or alternatives, but the guidance is clear and actionable.

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 declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds context about the algorithm and return format, consistent with read-only behavior. 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 well-structured with clear separation of modes and examples. Slightly long but every sentence adds information; 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?

For a tool with no output schema, the description mentions return type (ranked opportunities with reasoning). It covers the main behavior and edge cases (cross-event). Adequate.

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

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

Schema coverage is 100% with descriptions for both parameters. The description adds significant value beyond the schema by explaining modes and providing examples, clarifying when each parameter is used.

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: finding arbitrage opportunities via monotonicity violations. It distinguishes two modes (event vs topic) and explains the resource (Polymarket markets). No ambiguity.

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 explains when to use each mode, including why topic mode is needed for cross-event cases. It does not directly compare to sibling tools but provides sufficient guidance.

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

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

Annotations indicate readOnly, openWorld, non-destructive. Description elaborates with data sources (FRED, coinpaprika), processing steps (group by asset, fetch once, compute model probability), and output (top N by edge, trade 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 a single paragraph but is information-dense and front-loaded with the key action. It could be slightly more concise, but every sentence adds value regarding model, data, and 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 no output schema, the description sufficiently explains the return (top N ranked by edge with direction). It covers model, data sources, parameter defaults, and intended use. No major gaps for a discovery tool.

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

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

Schema coverage is 100% with good descriptions. The tool description adds context about how parameters relate to the ranking process (limit top N, window volume filter, min edge threshold) and mentions default values, enhancing understanding 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?

The description clearly states the tool scans high-volume Polymarket markets, identifies discrepancies between market price and Pipeworx model, and returns ranked edges. It specifies the model (lognormal from FRED + coinpaprika) and target domain (crypto-price bets), making it distinct from siblings like polymorph_arbitrage.

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

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

The description frames the tool for the 'what should I bet on today' question, implying it's for opportunity discovery without manual paging. It doesn't explicitly contrast with siblings or state when not to use, but the use case 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 the tool as read-only, idempotent, and non-destructive. The description adds valuable context: the typical spread range (2-25pp), the reason for the spread (different participant pools), and the return structure (leg-by-leg prices, spread in percentage points). 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 three sentences with a clear structure: purpose, reason for spread, modes, returns. It's front-loaded with the core purpose. While slightly verbose, every sentence earns its place by providing distinct information. Could be slightly tighter but 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 (two modes, three optional parameters) and the lack of an output schema, the description adequately explains the expected output and behavior. It covers what each mode does, provides examples, and describes the return format. A missing output schema is compensated by the description's detail.

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

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

Schema description coverage is 100%, with each parameter already described. The description enhances this by explaining how parameters interact (e.g., explicit parameters override topic-mapped ones) and providing concrete examples like 'fed' and 'KXFED-26OCT'. This adds meaningful context beyond the schema.

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

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

The description clearly defines the tool as a 'cross-venue spread between Kalshi and Polymarket', using specific verbs ('spread') and resources. It distinguishes itself from sibling tools like 'polymarket_arbitrage' and 'bet_research' by focusing on cross-venue differences, and explains the source of the spread (different participant pools).

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 outlines two usage modes ('topic' for pre-mapped macros, explicit parameters for custom pairings) with examples, providing clear guidance on when to use each. While it doesn't explicitly state when not to use the tool, the context implies it's for macro signals or custom pairs, and the sibling list suggests alternatives for other purposes.

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 provided, the description carries the full burden of behavioral disclosure. It explains the dual behavior (retrieve by key or list all) and persistence across sessions, which is valuable. However, it doesn't disclose error handling, performance characteristics, or what happens when a non-existent key is provided.

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 perfectly concise with two sentences that each earn their place. The first sentence explains the core functionality, and the second provides usage context. No wasted words, and information is front-loaded appropriately.

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 retrieval tool with no annotations and no output schema, the description provides adequate context about what the tool does and how to use it. The main gap is the lack of information about return format or error conditions, but given the tool's simplicity, this is acceptable.

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

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

The schema has 100% description coverage, so the baseline is 3. The description adds meaningful context by explaining the semantic effect of omitting the key parameter ('omit to list all keys'), which clarifies the tool's conditional behavior beyond what the schema alone 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 with specific verbs ('retrieve', 'list') and resources ('previously stored memory', 'all stored memories'). It distinguishes from sibling tools like 'remember' (store) and 'forget' (delete) by focusing on retrieval operations.

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

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

The description provides explicit guidance on when to use this tool ('to retrieve context you saved earlier') and when to use alternatives (implied by distinguishing from other memory tools). It also specifies the conditional logic: 'omit key' to list all memories versus providing a key to retrieve specific ones.

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 the full burden. It explains the parallel fan-out to multiple sources, the returned fields (structured changes, total_changes count, pipeworx:// URIs), and the input parameter formats. It does not mention potential errors, rate limits, or the single entity type limitation, but overall it provides good behavioral insight.

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, packed into a few sentences without unnecessary words. It front-loads the purpose, then details behavior, parameters, output, and use cases. Every sentence serves a 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?

Given no output schema, the description adequately describes the output format. It covers all parameters, entity type constraints, time window formats, and use cases. The tool is simple with 3 parameters, and the description completes the picture without gaps.

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

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

Schema coverage is 100% with descriptions. The description adds value by giving practical examples for each parameter: 'since' accepts ISO or relative dates with typical monitoring values, 'value' can be ticker or CIK, and 'type' is clarified as only 'company' supported. This extra context helps the agent use the tool correctly.

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: getting recent changes for an entity since a given time. It specifies the fan-out behavior for companies across multiple sources (SEC EDGAR, GDELT, USPTO) and provides example use cases like 'brief me on what happened with X', distinguishing it from sibling tools that provide static profiles or comparisons.

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 for "brief me on what happened with X" or change-monitoring workflows', giving clear usage context. It does not explicitly state when not to use or mention alternatives, but the context is sufficient for an agent to decide. Could be improved by noting that only 'company' type is supported.

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 provided, the description carries the full burden of behavioral disclosure. It effectively describes key traits: the tool performs a write operation ('Store'), specifies persistence behavior ('Authenticated users get persistent memory; anonymous sessions last 24 hours'), and hints at session scope. However, it does not cover potential errors, rate limits, or exact data formats beyond 'any text'.

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, followed by usage guidance and behavioral details in a logical flow. Both sentences earn their place by adding distinct value—no redundancy or waste. It is appropriately sized for a simple tool with two parameters.

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 low complexity (2 simple parameters, no output schema, no annotations), the description is largely complete. It covers purpose, usage, and key behavioral traits like persistence rules. However, it lacks details on return values or error handling, which could be useful despite the absence of an 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?

The input schema has 100% description coverage, clearly documenting both required parameters ('key' and 'value') with examples. The description adds minimal value beyond the schema, only reinforcing the general purpose without providing additional syntax, constraints, or usage details for the parameters. Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action ('Store a key-value pair') and resource ('in your session memory'), distinguishing it from sibling tools like 'forget' (remove) and 'recall' (retrieve). It provides concrete examples of what to store ('intermediate findings, user preferences, or context across tool calls'), making the purpose explicit and differentiated.

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 offers clear context on when to use this tool ('to save intermediate findings, user preferences, or context across tool calls'), but does not explicitly mention when not to use it or name alternatives. It implies usage for persistence needs but lacks direct comparison with siblings like 'recall' for retrieval or 'forget' for 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 effectively discloses that it accepts multiple input formats (ticker, CIK, name) and returns a set of canonical IDs and URIs, though it omits potential error cases or limitations.

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

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

Three sentences, each with a distinct purpose: purpose, version/example, return values. No wasted words, front-loaded with the main action.

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

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

Without an output schema, the description fully explains the return values (ticker, CIK, name, URIs) and notes versioning. It provides a complete picture for a simple lookup tool.

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

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

Schema coverage is 100%, baseline 3. The description adds value by explaining the accepted types for 'type' and providing examples for 'value', clarifying their semantics beyond the schema.

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

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

The description clearly states the tool resolves an entity to canonical IDs, with a specific verb and resource. It provides concrete examples and distinguishes from sibling tools focused on searching or asking.

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 mentions that the tool replaces 2–3 lookup calls, giving context on when to use it, but does not explicitly state when not to use it or mention alternative tools 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 declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. Description adds return format (ranked list with score, confidence, signal density) and process, which is valuable 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?

Three sentences: purpose, process, use case example. No fluff, every sentence adds value.

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

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

Covers all aspects: purpose, input params, process, output format (ranked list with scores). No output schema, so full burden is met. Given 4 params with 100% schema coverage, description is 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 3. Description adds meaning by clarifying 'entities' first entry is subject, rest competitors. Other params are adequately described in 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 'Compare AI visibility across multiple entities side-by-side', with specific verb and resource. Distinguishes from sibling 'ai_visibility_check' by emphasizing 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?

Explicitly says 'Useful for competitive AI-marketing audits' and gives example query. Does not state when not to use, but sibling context implies alternative for single entity.

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 provided, the description carries full burden. It discloses the search behavior and return fields (display name, ORCID, institution, works count, citation count), which is valuable. However, it doesn't mention rate limits, authentication requirements, pagination, or error conditions that would be important for a search tool.

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, efficient sentence that front-loads the core purpose and includes essential return information. Every word earns its place with zero waste or 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?

For a search tool with no annotations and no output schema, the description provides basic purpose and return fields but lacks important context like result format, error handling, or performance characteristics. It's minimally adequate but has clear gaps in behavioral transparency.

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 fully documents both parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions. Baseline 3 is appropriate when the schema does all the parameter documentation work.

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 specific action ('Search researchers and authors by name'), resource ('in OpenAlex'), and distinguishes from siblings by focusing on authors rather than concepts, institutions, or works. It provides a precise verb+resource combination with clear 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 implies usage context by specifying 'by name in OpenAlex' and listing returned fields, but doesn't explicitly state when to use this tool versus alternatives like search_institutions or search_works. No explicit guidance on when-not-to-use or named alternatives is provided.

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 the full burden of behavioral disclosure. It mentions the return fields but does not describe key behavioral traits such as pagination, rate limits, authentication needs, error handling, or whether the search is case-sensitive. For a search tool with zero annotation coverage, this leaves significant gaps in understanding how the tool behaves beyond basic functionality.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose, resource, search criteria, and return fields without any wasted words. It is front-loaded with essential information and appropriately sized for the tool's complexity.

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

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

Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and return fields, but lacks details on behavioral aspects (e.g., pagination, errors) and does not fully compensate for the absence of annotations and output schema, leaving some contextual gaps for effective agent use.

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

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

Schema description coverage is 100%, with clear descriptions for both parameters (query and limit). The description adds minimal value beyond the schema by specifying the resource ('academic institutions') and example ('e.g., "MIT"'), but it does not provide additional semantic context like search algorithm details or result ordering. Baseline 3 is appropriate as the schema does the heavy lifting.

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 specific action ('Search academic institutions'), resource ('in OpenAlex'), and scope ('by name'), distinguishing it from sibling tools like get_concept, search_authors, and search_works. It explicitly mentions what fields are returned (name, country, type, works count, top concepts), making the purpose unambiguous and well-defined.

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

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

The description implies usage for searching institutions by name in OpenAlex, but it does not provide explicit guidance on when to use this tool versus alternatives (e.g., get_concept for concepts, search_authors for authors, search_works for works). No exclusions or prerequisites are mentioned, leaving the context somewhat open-ended without clear differentiation from siblings.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the return fields (title, authors, etc.) but lacks critical details such as pagination behavior, rate limits, authentication requirements, or error handling, which are essential for a search tool.

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

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

The description is a single, well-structured sentence that efficiently conveys the tool's purpose and return values without any wasted words. It is front-loaded with the core action and resource, making it easy to understand quickly.

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

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

Given the tool's moderate complexity (search with two parameters), no annotations, and no output schema, the description is minimally adequate. It covers the basic purpose and return fields but lacks details on behavioral traits and usage guidelines, leaving gaps in completeness for effective tool invocation.

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 both parameters (query and limit) adequately. The description does not add any additional meaning or context beyond what the schema provides, such as query syntax examples or limit implications, resulting in a baseline score of 3.

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

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

The description clearly states the specific action ('Search'), resource ('scholarly works (papers, books, datasets)'), and scope ('in the OpenAlex index'), distinguishing it from sibling tools like get_concept, search_authors, and search_institutions by focusing on works rather than concepts, authors, or institutions.

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

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or comparisons with sibling tools, leaving the agent to infer usage based solely on the tool name and description.

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 provides good transparency: it discloses the data sources (SEC EDGAR + XBRL), the output fields, and the fact that it replaces multiple steps. It does not mention rate limits or authentication, but the behavior is adequately described for a read-only analysis tool.

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 concise sentences with no redundancy. The first sentence immediately states the core purpose. Each sentence adds essential information (domain, outputs, efficiency benefit). Every word earns its place.

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

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

For a single-parameter tool with no output schema, the description is remarkably complete. It covers input domain, output structure, and the tool's role in the workflow. The lack of error handling notes is acceptable given the tool's simplicity.

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 example claims and clarifying the domain (financial claims) beyond the schema's generic string description. This enhances the agent's understanding of what constitutes a valid input.

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 fact-checks natural-language claims, specifies the domain (company-financial for US public companies), and lists the exact output format (verdict, structured form, actual value with citation, percent delta). This makes it distinct from siblings like ask_pipeworx which is more general.

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

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

The description implies usage for financial claims and notes it replaces multiple agent calls, but it does not explicitly state when not to use it or suggest alternatives (e.g., ask_pipeworx for non-financial claims). More explicit guidance would be helpful.

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