Ai Briefing

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

The description explains that Pipeworx selects the right tool and fills arguments, revealing internal orchestration. Since no annotations are provided, this disclosure is valuable. It does not mention limitations like rate limits or data recency, but the behavioral promise is clear.

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: purpose, mechanism, and examples. Every sentence adds value, no fluff. Front-loaded with the core action and clearly structured.

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

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

Given the single parameter, no output schema, and no annotations, the description adequately explains what the tool does and how to use it. It could mention return format or error cases, but for a simple question-answering tool, the completeness is sufficient.

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

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

The schema description for the single parameter 'question' is minimal ('Your question or request in natural language'), and the tool description adds the context of using 'plain English' and examples. With 100% schema coverage, a baseline of 3 is appropriate; the description adds moderate value beyond the schema.

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

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

The description clearly states it answers plain English questions by selecting the best data source and filling arguments. It provides concrete examples, making its purpose and scope immediately understandable.

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 advises to 'just describe what you need' and provides examples, but does not explicitly state when not to use this tool or mention alternatives among siblings. However, its purpose is distinct from sibling tools like 'search_developments' or 'get_ai_news', so usage 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 indicate safe read (readOnlyHint=true) and open world (openWorldHint=true). Description adds significant behavioral context: it fans out to packs, classifies bets, and returns a model-vs-market 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?

Medium-length description (~150 words). Informative but slightly promotional ('core demo product' sentence is redundant). Good front-loading of purpose but could tighten wording.

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 2 parameters and no output schema, description explains internal fan-out, classification, and output structure. Only missing explicit return format, but otherwise 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?

Schema coverage is 100%, so baseline is 3. Description adds meaning: explains market parameter accepts slug, URL, or question text; notes depth default (thorough). Adds value beyond schema by clarifying input flexibility.

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

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

Description clearly states it researches Polymarket bets by pulling Pipeworx data, resolving market, classifying, fanning out to packs, and returning evidence packet and comparison. Very specific verb+resource, distinguishes from siblings like validate_claim or ask_pipeworx.

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

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

Explicitly lists use cases ('should I bet on X?', 'what does the data say...', 'is there edge...') and notes it is the core demo product, implying advantage over manual pack discovery. No explicit when-not, but context suggests alternatives 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?

No annotations provided, so description carries full burden. It discloses data sources (SEC EDGAR, FDA) and return format (paired data + URIs). However, it does not mention rate limits, authentication, or potential failure modes (e.g., invalid tickers). 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?

Three sentences, each earning its place: purpose, type-specific breakdown, and efficiency. No fluff, well-structured, and front-loaded with 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?

Given no output schema and only 2 params, description explains what data is returned for each type. It could mention error handling or prerequisites (like ticker validity), but overall complete for a comparison tool with good parameter coverage.

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. Description adds significant value by explaining the type-specific behavior and providing examples (e.g., 'AAPL', 'ozempic'). Baseline 3, upgraded for extra context beyond schema.

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

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

Description clearly states verb 'Compare', resource 'entities', and scope 'side by side in one call'. It distinguishes by entity type and data returned, but does not explicitly differentiate from sibling tools. Sibling tools like search_developments or get_timeline do not offer comparison, so the purpose is clear enough.

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 explains when to use: to compare 2-5 entities, with specific data per type. It also highlights efficiency gains (replaces 8-15 calls). However, it does not state when NOT to use or mention alternatives, though context implies single-entity calls for non-comparison needs.

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 the description carries the full burden. It reveals the tool is a search/discovery tool (read-only), mentions it returns tools with names and descriptions, and indicates it ranks results by relevance. No destructive or side-effect behavior is implied. Could add details about result ordering or lack of filtering, but the current text 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?

Two sentences, zero wasted words. First sentence states purpose and return value. Second sentence gives critical usage guidance. Perfectly front-loaded and concise.

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

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

Given the tool's simplicity (2 params, no output schema, no nested objects), the description fully covers what the agent needs to know: what it does, when to use it, what it returns, and how to use the parameters. 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?

Schema coverage is 100%, so baseline is 3. The description adds no additional meaning beyond what the schema provides: the query parameter is described as a natural language description, and limit has a default and max. No extra context is needed.

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: to search the tool catalog by describing needs, returning the most relevant tools with names and descriptions. It distinguishes itself from siblings by specifying a discovery/selection use case rather than asking questions or retrieving news.

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

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

Explicitly instructs to 'Call this FIRST when you have 500+ tools available and need to find the right ones for your task.' This gives clear when-to-use guidance and implies it should be used before other tools in the server.

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 must disclose behavior. Describes returned data and citation URIs but does not explicitly state that the operation is read-only or mention any side effects, auth requirements, or rate limits. Adequate but lacks explicit safety cues.

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

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

Concise 4-sentence description, front-loaded with core purpose. Each sentence provides distinct value: purpose, parameters, data sources, usage guidance. No unnecessary words.

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

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

Tool is complex (multiple data sources); description covers returned data, citation URIs, and usage guidance. No output schema, but description explains return format. Could mention result limits or performance, but overall sufficient.

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

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

Schema coverage is 100%, and description adds significant meaning: explains 'type' only supports 'company' and that 'value' accepts ticker or CIK but not names, directing users to resolve_entity. Goes 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?

Clearly states 'Full profile of an entity' and lists specific data sources (SEC filings, XBRL, patents, news, LEI). Differentiates from sibling tools by noting it replaces 10-15 sequential calls and directs to usa_recipient_profile for federal contracts.

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 (company entities with ticker or CIK) and when not (federal contracts). Provides alternative tool (usa_recipient_profile) and prerequisite (resolve_entity for names).

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 must cover behavioral traits. It states 'Delete' which implies mutation, but doesn't disclose if the operation is irreversible, requires confirmation, or affects related data. It lacks transparency 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 sentence with no extraneous words, efficiently conveying the core action and resource.

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

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

Given the tool's simplicity (1 param, no output schema, no annotations), the description is adequate but lacks completeness regarding side effects or return values. It covers the basic operation.

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, so the description adds little beyond stating the parameter's purpose. Baseline 3 is appropriate since the schema already explains the key parameter.

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

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

The description clearly states the verb 'delete' and the resource 'stored memory by key'. It distinguishes from sibling tools like 'remember' (store) and 'recall' (retrieve), but doesn't explicitly differentiate from any other deletion tool.

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 a memory needs to be removed, but provides no guidance on when not to use it or alternatives. It doesn't mention if there are side effects or prerequisites.

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

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

With no annotations provided, the description must disclose behavior. It does so by specifying the scope (industry news, not toolbelt) and providing examples. However, it doesn't mention that the tool is a read operation, whether it's destructive, or any other behavioral traits like rate limits or data freshness. Still, it adds value beyond the schema.

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: the first clearly states the purpose and examples; the second distinguishes from sibling tools. No unnecessary words, every sentence adds value. Highly concise and front-loaded with the core 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?

Given low complexity (2 optional params, no output schema), the description is nearly complete. It explains the scope, what's included, and what's not. Missing explicit mention that output is a list of news items, but that's implied. Could mention that it returns a summary or articles, but for a simple news tool, it's 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 description coverage is 100%, so baseline is 3. The description adds meaning by implying the parameters control time range and result count (via context 'AI industry news'). It doesn't explicitly detail parameters, but the schema already describes them well. The description's context enhances understanding, earning a 4.

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 'Get' and resource 'AI industry news' with specific examples of what is included (model releases, funding rounds, acquisitions, policy changes, benchmark results). It also distinguishes itself from sibling tools like 'get_ai_toolbelt' by explicitly saying it's 'separate from toolbelt; this is about what happened in the AI industry, not tools you can use.'

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 ('Get AI industry news') and provides clear exclusions ('separate from toolbelt; this is about what happened in the AI industry, not tools you can use'), directly distinguishing it from the sibling 'get_ai_toolbelt'. No other sibling needs differentiation as the focus 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?

With no annotations, the description carries the full burden. It discloses that the tool returns new features and tools since the training cutoff, and hints at temporal filtering ('latest'). It doesn't detail return format or pagination, but the input schema covers the parameters, and the tool appears to be a read-only retrieval, which is implied. 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 two sentences, front-loaded with the key purpose, and every word adds value. No fluff or repetition.

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

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

Given the tool has no required parameters and a simple input schema, the description is complete enough to understand the tool's purpose and usage. It could mention output format but is not essential for a list-retrieval 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 parameters are already well-documented in the schema. The description does not add any additional meaning beyond the schema; it only mentions 'latest' but doesn't elaborate on the parameter values. Baseline 3 is appropriate.

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

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

The description clearly states the verb ('Get') and the resource ('latest tools, features, and capabilities'), and distinguishes the tool from siblings by emphasizing 'new tools in your toolbelt' and mentions specific categories (Claude Code features, MCP servers, SDK updates, CLI tools, integrations). This sets it apart from other tools like 'get_ai_news' or 'discover_tools'.

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

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

The description explicitly says 'Call this to discover what new tools are in your toolbelt since your training cutoff', which provides clear context for when to use the tool. However, it does not explicitly state when not to use it or provide alternative tools, which would have earned a 5.

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

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

The description discloses the tool retrieves data from the last 24 hours and defaults to today, but does not mention any behavioral traits like caching, rate limits, or data freshness beyond 'last 24 hours.' Since there are no annotations, the description partially compensates but lacks deeper behavioral context.

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

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

Two sentences efficiently convey the tool's purpose and usage recommendation without extraneous information. The call to action is 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?

The description is complete for a simple, read-only tool with one optional parameter and no output schema. It explains what the briefing contains and when to use it, though it could mention the output format (e.g., 'returns a list of tool names with 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%, and the description adds context that the date parameter defaults to today and filters to the last 24 hours, going beyond the schema's basic description of 'Date in YYYY-MM-DD format (default: today).'

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 'today's AI tools briefing' with specific content categories (MCP servers, APIs, SDKs, etc.), and it distinguishes from siblings like get_ai_news by focusing specifically on developer tools released in the last 24 hours.

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 calling this 'at the start of any session to discover new tools,' providing clear when-to-use guidance. It also implies this is for discovering tools, contrasting with other tools like search_developments that likely handle historical or specific searches.

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 describes the output (models, companies, capabilities) but doesn't disclose behaviors like whether it caches results, the freshness of data, or if it covers all sources. It's adequate but not thorough for a read tool with no annotations. Score 3.

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

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

Two sentences with clear front-loading: first sentence states purpose, second gives usage context. No wasted words. Could be slightly more concise, but very efficient. Score 4.

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

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

With only one optional parameter and no output schema, the tool is simple. The description covers the purpose and high-level output. However, it lacks details on what constitutes 'recent', whether results are sorted, or any pagination. For a simple tool, it's adequate but not complete. Score 3.

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

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

Schema description coverage is 100% for the single parameter 'days'. Description does not add any additional semantics beyond what the schema already provides (default 30). Baseline 3 is appropriate.

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

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

Description clearly states the tool retrieves recent AI model releases with details on companies and capabilities. The verb 'get' and resource 'model releases' are specific. However, it doesn't explicitly distinguish from siblings like get_ai_news or get_recent, which could also cover model releases, so a 4 is appropriate.

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 implies usage when needing to know what models are available ('what's available to build with right now'), but provides no explicit guidance on when not to use it or alternatives. With siblings like get_ai_news or search_developments, more precise guidance would help. Score 3 for implied but incomplete 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, the description carries full burden. It explains filtering options but does not disclose behavioral traits like default values, result ordering, or pagination. The description adds value by listing categories and sources, but lacks details on rate limits or data freshness.

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

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

The description is concise and front-loaded with the tool's purpose. It lists categories and sources efficiently. However, the listing could be shortened if it's redundant with enum values in the schema, but since no enums are defined, the listing is valuable.

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

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

Given the tool has no output schema and is a filtering tool, the description is adequate but not thorough. It explains input options but doesn't describe the output format or provide examples. The tool seems simple enough, but could be more helpful with usage tips.

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 parameters are documented in the schema. The description adds no additional parameter meaning beyond listing categories and sources, but that is already present in schema descriptions. 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 retrieves recent tool and API releases with filtering by category, source, or timeframe. It lists many categories and sources, providing specificity. However, it doesn't distinguish from sibling tools like get_ai_news, which might also retrieve recent news.

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 filtering recent releases, but it doesn't explicitly state when to use this tool versus alternatives. For example, it doesn't mention that get_ai_news might be more appropriate for general AI news or that search_developments might be used for broader search.

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 mentions chronological order and date range but does not disclose pagination behavior, whether results are sorted ascending/descending, or any rate limits. It is adequate but not detailed.

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

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

The description is a single concise sentence plus a brief usage hint. It is front-loaded and wastes no words.

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

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

Given no output schema, the description does not explain what the timeline data looks like (e.g., event objects with date, title, summary). It is minimally complete but lacks detail about the return format.

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 does not add additional meaning beyond the schema, but the schema itself is clear. No extra semantic help for the agent.

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

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

The description clearly states the tool retrieves a chronological timeline of AI developments between two dates, which distinguishes it from sibling tools like get_ai_news or search_developments. However, it does not explicitly differentiate from get_recent, which may also cover timelines.

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 says 'useful for understanding what happened during a specific period,' which implies when to use it. But it does not provide guidance on when not to use it or alternatives (e.g., search_developments for non-chronological 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 provided, so description carries full burden. Discloses rate limit (5 messages per identifier per day) and that it's free. No contradiction; could add more about data handling but 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?

Two sentences plus a rate-limit note are highly concise. No fluff, every sentence adds value. Front-loaded with 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?

Tool is simple; description covers purpose, usage, and constraints comprehensively. No output schema needed; return behavior is implied.

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. Description adds value by explaining how to write the message and specifying the rate limit, which is extra 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?

Clearly states 'Send feedback to the Pipeworx team' and lists specific use cases (bug reports, feature requests, missing data, praise). Distinguishes from sibling tools focused on queries or data retrieval.

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

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

Specifies what to include (describe tool/data context) and what not to include (end-user's prompt). Mentions rate limit. Lacks explicit when-not-to-use, but context implies it's strictly for feedback.

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

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

Annotations already declare readOnlyHint and destructiveHint, assuring the tool is safe. The description adds behavioral details: walking child markets, extracting dates/thresholds, sorting, and reporting violations. This adds context beyond annotations, though it does not cover edge cases like no arbitrage found.

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 multi-sentence but each sentence contributes meaning. It is front-loaded with the core purpose and uses an example to illustrate the concept. Slightly verbose 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?

The tool has one parameter, no output schema, but the description clearly states the return format (list of {market_a, market_b, gap_pp, suggested_trade}). The logic is fully explained, and annotations cover safety. No gaps remain.

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

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

The single parameter 'event' is described in both schema and description. Schema coverage is 100%, so baseline is 3. The description adds no extra format or example beyond what the schema 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 finds arbitrage opportunities via monotonicity violations within a Polymarket event. It provides a specific verb-resource pair and a detailed example, making the purpose unmistakable even without explicit sibling differentiation.

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

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

The description tells the user to pass a Polymarket event slug or URL and explains the scenario (multiple date/threshold markets). It implicitly indicates when to use (for intra-event arbitrage) but does not explicitly state when not to use or mention alternative tools 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 indicate readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: V1 covers crypto bets, uses lognormal model from FRED + live coinpaprika price, scans top markets, groups by asset, fetches each asset's price history once, computes model probability per market, ranks by |edge|. No contradiction.

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

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

The description is a single well-structured paragraph of about 150 words. It front-loads the core action, then explains the model and process, and ends with output. 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 tool's complexity (fetching data, model computation, ranking) and no output schema, the description is thorough. It explains the model sources (FRED, coinpaprika), process (group by asset, fetch once, compute probability), and output format (top N edges with trade direction). Sufficient for agent 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 covers all 3 parameters with descriptions. The description adds value by providing defaults (limit default 10, max 25; window default 1wk; min_edge_pp default 0.5) and clarifying ranking by edge magnitude. Schema coverage is 100%, so baseline is 3; description enhances to 4.

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 where Pipeworz data disagrees with market price, and returns top N edges with trade direction. It uses specific verbs and resource, and distinguishes from siblings like polymarket_arbitrage.

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

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

The description explicitly says it's built for the 'what should I bet on today' question, helping users discover opportunities without manual paging. It implies when to use but does not explicitly state when not to use or list alternatives. The purpose is distinct enough 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?

No annotations are provided, so the description carries the full burden. It discloses the core behavior (retrieve by key or list all) but does not mention any side effects, persistence details, or performance implications. Adequate but not rich.

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 the core action, no wasted words. Perfectly concise.

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

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

Given no output schema and simple parameter, the description is sufficient for a retrieval tool. It does not explain the format of returned memories or whether they include timestamps, but these are reasonable omissions for a simple 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% with one parameter clearly documented. The description adds context that omitting the key lists all memories, which aligns with the optionality in the schema and adds 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 tool retrieves a stored memory by key, or lists all if key is omitted. It distinguishes itself from sibling tools like '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?

The description explicitly says to use this to retrieve context saved earlier, implying when to use. It does not mention when not to use or alternatives, but the context is clear given sibling names.

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?

Despite no annotations, the description fully discloses the tool's behavior: it fans out to multiple data sources (SEC, GDELT, USPTO) in parallel, accepts date formats (ISO or relative), and returns structured output with counts and URIs. This is comprehensive for a read-only 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 concise—3-4 sentences—and well-structured: purpose, data sources, parameter details, return format, and usage examples. Each 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?

For a tool with no output schema, the description adequately explains the return format (structured changes, total_changes count, URIs). It also covers the parallel fan-out behavior, making it complete for an AI agent to invoke 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?

The input schema already has 100% coverage and provides clear descriptions for all parameters. The description adds minor context (e.g., example date formats, specific values for 'value') but does not significantly augment 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 clearly states the tool's purpose: finding what's new about an entity since a given time. It uses a specific verb ('what's new') and resource ('entity'), and the example 'brief me on what happened with X' gives concrete use cases. It implicitly differentiates from sibling tools like 'get_recent' or 'remember' by focusing on change detection.

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 the tool for 'brief me on what happened with X' or change-monitoring workflows, providing clear context. However, it does not mention when NOT to use it or give alternatives, leaving some room for mis-selection in ambiguous cases.

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 description carries full burden. It discloses persistence behavior (authenticated gets persistent, anonymous lasts 24 hours). It does not mention overwrite behavior (key collision), size limits, or concurrency effects, leaving some behavioral traits unspecified.

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 earning its place: first states purpose, second gives usage context, third clarifies persistence. No wasted words. Front-loaded with the core 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?

Given the simplicity (2 required params, no output schema), the description covers purpose, usage, and persistence adequately. It could mention overwrite behavior, but overall it's sufficient for an agent to use correctly.

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

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

Schema coverage is 100%, so baseline is 3. The description adds meaning by providing example keys (subject_property, target_ticker, user_preference) and explaining value as 'any text — findings, addresses, preferences, notes'. This goes beyond the schema's generic 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 'store' and resource 'key-value pair in session memory', and distinguishes from siblings like 'recall' (retrieve) and 'forget' (delete). It also specifies use cases: 'save intermediate findings, user preferences, or context across tool calls'.

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

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

The description implies when to use (store context across calls) and mentions memory persistence differences (authenticated vs anonymous). However, it does not explicitly state when not to use or name alternatives, though siblings like 'recall' and 'forget' are obvious.

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 mentions the tool is a single call and returns specific fields, but does not disclose side effects, error handling, authentication requirements, or whether it is read-only. The behavioral transparency is insufficient.

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

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

The description is two sentences, front-loaded with the main action, and every 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?

The description covers the tool's purpose and output fields but lacks details on return format, error behavior, and validation. Given no annotations or output schema, it is moderately complete but not fully 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 description coverage is 100% with both parameters described. The description adds value by explaining v1 supports 'company' for type and providing examples for value, enhancing understanding 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 entities to canonical IDs, specifies supported type 'company' and input formats (ticker, CIK, name). It lists output fields and distinguishes from siblings by mentioning it replaces multiple lookup calls.

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

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

The description implies usage by stating it replaces 2–3 lookup calls, but does not explicitly state when to use it vs alternatives or provide when-not-to-use guidance. Usage context is implied rather than explicit.

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 present, so the description carries the full burden. It clearly indicates the tool is a search across multiple sources, implying no destructive side effects. It adds context about return content (matching developments), which is useful for understanding behavior.

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

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

The description is concise (two sentences) and front-loaded with the core purpose. Every sentence provides valuable context, 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?

The description is sufficient for a straightforward search tool with no output schema. It explains the sources and provides query examples. However, it could mention pagination or result format if relevant, but given the simplicity, it is mostly 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%, and the description does not add any new information about the parameters beyond what the schema provides. The baseline is 3, and the description does not elevate it.

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

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

The description clearly states the tool searches for new tools, APIs, MCP servers, and frameworks by keyword, and lists specific sources (HN, GitHub, HuggingFace, AI company blogs). It distinguishes itself from sibling tools like discover_tools or get_recent by focusing on development discovery.

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

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

The description provides example queries ('new MCP servers', 'vector database tools'), which guide appropriate usage. However, it does not explicitly state when NOT to use this tool or compare it directly to siblings, leaving some ambiguity.

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

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

Since no annotations are provided, the description carries full responsibility. It clearly outlines the tool's behavior: returns a verdict, structured claim form, actual value with citation, and percent delta. It also discloses the version 1 scope limitations (only company-financial claims). 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 concise, front-loading the core purpose in the first sentence, then adding specific domain and return details. 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?

Given the simple input schema (one parameter, no output schema, no annotations), the description covers purpose, scope, return values, and even version limitations. It lacks mention of error handling or edge cases, but for a tool with straightforward input, this is nearly complete.

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

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

Schema description coverage is 100%, and the description adds only a brief example beyond the schema's own description. The example is helpful but does not significantly enhance semantic understanding beyond what the schema already provides. 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 explicitly states it fact-checks natural-language claims against authoritative sources, specifies the domain (company-financial claims via SEC EDGAR+XBRL), and lists the verdict types and return format. It clearly distinguishes itself from sibling tools like ask_pipeworx and search_developments by focusing on structured fact-checking with citations.

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 verifying financial claims and notes it replaces multiple sequential calls, but it does not explicitly state when not to use this tool or suggest alternative tools for non-financial or other claim types. Guidance is present but indirect.

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 are empty, so the description must carry the full burden. It states it returns 'the most relevant tool-related developments', but doesn't disclose limits on results, whether it uses a fixed dataset or live search, or if the response format is always text. This is adequate but not exhaustive for an unannotated 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 concise (three sentences), front-loaded with the core purpose, and each sentence adds value. The only minor issue is that the last sentence ('Returns the most relevant...') could be more specific, but it doesn't waste space.

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

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

Given the tool has only 2 parameters (both well-documented in schema) and no output schema, the description is fairly complete. It explains the input format with examples. However, it doesn't clarify whether it supports follow-up questions or if the output is limited to a certain number of results, but these are minor 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 description coverage is 100%, so the schema already documents both parameters. The description adds example questions (which maps to 'question') but doesn't elaborate on 'days' beyond its schema description. Baseline 3 is appropriate since the description adds no new semantic value beyond 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?

The description clearly states the tool's purpose: answering natural language queries about recent tools and developments. It provides specific example queries ('any new MCP servers this week', 'latest Claude tools') which make the function highly specific and distinguishable from siblings like 'get_ai_news' or 'get_recent'.

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 through examples ('Ask...'), giving the agent clear context on when to use this tool. However, it does not explicitly state when not to use it or provide alternatives among the listed siblings (e.g., 'get_ai_news' might be preferred for non-tool-specific news).

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