Uk Parliament

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

The description explains how the tool works internally (routing, argument filling) and what it returns (structured answer with stable pipeworx:// citation URIs). This adds significant context beyond the readOnlyHint annotation, which only indicates it is a read operation.

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

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

The description is a single, well-structured paragraph that front-loads the key instruction ('PREFER OVER WEB SEARCH'), lists supported content types concisely, and gives concrete examples. It is informative but could be slightly trimmed without losing meaning.

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 (one string parameter) and no output schema, the description compensates thoroughly by explaining the tool's capabilities, use cases, and expected output. It leaves no ambiguity about when and how to use the tool.

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

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

The schema coverage is 100% for the single 'question' parameter, and its description is straightforward. The tool description adds value by providing examples and specifying the types of questions that are appropriate, leveraging the parameter without needing additional schema detail.

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

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

The description clearly states that ask_pipeworx routes factual questions to the appropriate specialized tool among over 1,423 tools, and explicitly says to prefer it over web search. It distinguishes itself from sibling tools by being a general query interface, while siblings are specific (e.g., get_bill, search_bills).

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 to prefer this tool over web search for questions involving structured data, and lists many example domains and query types. It does not explicitly state when NOT to use it or mention alternatives, but the examples cover a wide range, making usage clear.

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

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

Annotations declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds context: it resolves the market, classifies the bet, fans out to specific data packs (e.g., crypto+fred+gdelt for BTC), and returns a comparison. This goes beyond annotations, providing a clear mental model of the tool's behavior without contradicting 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 4-5 sentences with key information front-loaded. It efficiently covers purpose, input, process, and use cases without extraneous detail. Could be slightly more structured (e.g., bullet points for use cases) but overall concise and informative.

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

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

Given the tool's complexity (multiple input types, classification, fan-out to various data packs), the description covers essential aspects. It describes the return (evidence packet + market-vs-model comparison) without needing an output schema. Adequate for agent understanding.

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 description explains the meaning of both parameters: depth (quick vs thorough with defaults and implications) and market (three input formats). This adds value beyond the schema, helping the agent understand how to use them effectively.

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

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

Description clearly states it researches a Polymarket bet by pulling relevant Pipeworx data. It specifies three input formats (slug, URL, question text) and outlines the process: resolving market, classifying bet, fanning out to data packs, returning evidence packet and comparison. This distinguishes it from sibling tools like ask_pipeworx or compare_entities.

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

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

Explicit use cases are provided: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. It contrasts with agents having to discover packs themselves, implying this is the preferred tool for bet research. Missing explicit when-not-to-use but guidance is strong.

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 provide readOnlyHint=true, so safety is clear. The description adds context about which stages are included, which is useful but does not disclose additional behavioral traits like ordering or filtering.

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

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

Single sentence, front-loaded with purpose, no wasted words. Efficient 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 a simple tool with one parameter and no output schema, the description is adequate but leaves gaps (e.g., output format, whether past or future stages). It covers the basics but could be more 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 0%, and the description does not explain the bill_id parameter. The description implies it's needed ('All stages of a bill') but does not confirm or add format or constraints 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 returns all stages of a bill, listing examples like introduction, readings, committee, and royal assent. This is specific and distinct from sibling tools like get_bill (which likely returns bill details) and search_bills.

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

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

No guidance on when to use this tool versus alternatives. The description implies it's for bill stages but does not specify context or exclusions (e.g., 'use get_bill for other bill info').

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, and the description adds valuable behavioral context: data sources (SEC EDGAR/XBRL, FAERS), what data is returned (revenue, adverse events, etc.), and output format (paired data + pipeworx:// URIs). No contradictions.

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

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

The description is front-loaded with purpose, then usage triggers, then per-type details. At 4 sentences, it's efficient but could be slightly tighter (e.g., 'Replaces 8–15 sequential agent calls' could be implied). Still very clear.

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

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

Without output schema, the description mentions 'paired data + pipeworx:// citation URIs' but lacks specifics on structure (e.g., JSON format, sorting). However, for a comparison tool with two types, the coverage of input and behavior is high.

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%, yet the description adds substantial meaning: for 'type' it clarifies the domain and data pulled; for 'values' it gives examples and specifies tickers/CIKs for companies vs drug names. This goes well beyond schema descriptions.

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

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

The description clearly states the tool compares 2–5 companies or drugs side by side, with specific verb 'Compare' and resource 'companies (or drugs)'. It distinguishes from siblings by noting it replaces 8–15 sequential calls, and scope is explicit (financial vs regulatory data).

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 trigger phrases ('compare X and Y', 'X vs Y', etc.) and separates use by type ('company' vs 'drug'). It lacks explicit 'when not to use' or direct mention of alternatives like entity_profile, but the intent 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 mark it as read-only. The description adds that it returns 'top-N most relevant tools with names + descriptions,' which is useful behavioral context beyond the annotation. 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?

Description is concise (3-4 sentences) and front-loaded with the key purpose. Slightly verbose with the list of example domains, but each sentence contributes 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 low complexity, no output schema, and sufficient annotations, the description provides complete context: what it does, when to use, what it returns, and how to call it.

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 tool description adds context that 'query' is a natural language description and hints that 'limit' controls N, but adds limited value beyond the schema.

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

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

The description clearly states the tool finds tools by describing a data or task, and distinguishes it from sibling tools by instructing to call it first to see the option set. The verb 'Find' and resource 'tools' are specific and unambiguous.

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

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

Explicitly says when to use (browse, search, look up, discover tools for many domains) and when not (not for answering a single query, but to explore options). Also provides concrete examples of data domains and the instruction to call it first.

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

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

Annotations already indicate readOnlyHint=true, and the description adds further behavioral details: returns SEC filings, fundamentals, patents, news, LEI, and citation URIs. 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 (4 sentences) and well-structured: main purpose, usage guidance, return details, and input clarifications. Every sentence serves a clear purpose.

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

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

Despite lacking an output schema, the description adequately explains the return values and their sources. It covers the aggregation of multiple data types and the use of citation URIs.

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 value beyond the schema by explaining the type parameter's limitations (only company) and the value parameter's acceptable formats (ticker or CIK, not names).

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 'Get everything about a company in one call,' specifying a verb, a resource, and scope. It distinguishes itself from siblings like resolve_entity and compare_entities by mentioning typical usage scenarios.

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

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

The description explicitly tells when to use (e.g., 'tell me about X' queries) and when not to (if you have a name, use resolve_entity first). It also notes that using this tool avoids calling 10+ other tools.

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

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

Annotations already show readOnlyHint=false, indicating mutation. Description only adds 'delete' and 'clear', but no extra behavioral context (e.g., irreversibility, permissions, or effects on other stored data).

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

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

Two concise sentences with no wasted words. Front-loaded with purpose and usage.

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

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

Given the simple tool (1 param, no output schema), description covers purpose and usage. Lacks details like behavior on missing key or confirmation, but adequate for such 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 coverage is 100%, so baseline is 3. Description adds 'by key' but does not elaborate beyond the schema's description of 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?

Description clearly states 'Delete a previously stored memory by key', which is a specific verb+resource. It distinguishes from siblings like remember and recall, which store or retrieve memories.

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

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

Explicitly says when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also pairs with remember and recall, though no when-not or alternative tools are mentioned.

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

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

Annotations already mark this as read-only. The description adds no additional behavioral context, such as what data is returned or any constraints.

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

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

The description is very concise (two words) but lacks substance. It does not fully earn its place as it omits critical details.

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

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

For a simple tool with one required parameter and no output schema, the description should at least explain the return value and parameter meaning. This is not provided.

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 description provides no information about the bill_id parameter, leaving the agent to rely solely on the schema, which has 0% coverage in the description.

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

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

The description 'Bill detail.' is a minimal phrase that echoes the tool name but adds little. It does not specify the action or differentiate from sibling tools like bill_stages.

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

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

No guidance is given on when to use this tool versus alternatives such as search_bills or bill_stages.

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 readOnlyHint annotation already indicates this is a safe read operation. The description adds minimal behavioral context beyond stating it returns 'member detail'. It does not disclose potential pagination, rate limits, or error handling.

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

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

The description is extremely concise, consisting of only four words. While this avoids fluff, it could be slightly expanded for clarity without sacrificing brevity.

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 and no output schema, the description vaguely states 'member detail' without specifying what fields are returned. This leaves ambiguity about the response structure, which could be addressed.

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

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

Both parameters have schema descriptions (100% coverage), so the description adds no additional meaning. It does not elaborate on the format of 'includes' or expected use cases for each 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 tool retrieves member details by ID. It uses a specific verb ('get') and resource ('member'), making the purpose straightforward. However, it does not differentiate from sibling tools like search_members or entity_profile.

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

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

No guidance is provided on when to use this tool versus alternatives. There is no mention of when not to use it or any prerequisites, leaving the agent to infer context from the name alone.

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 only include readOnlyHint=false, which is minimal. The description adds critical behavioral context: 'Rate-limited to 5 per identifier per day. Free; doesn't count against your tool-call quota.' This goes well beyond what annotations provide without contradiction.

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

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

The description is moderately concise (about 5 sentences) and front-loaded with the core purpose. Every sentence contributes meaningful guidance (use cases, content guidelines, limitations). Minor redundancy in listing types could be tightened.

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 (feedback submission) and absence of output schema, the description adequately covers usage, content expectations, and behavioral constraints. It does not explain return values, but that is unnecessary for a tool with no output schema.

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

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

Schema coverage is 100%, so the schema already describes all parameters. However, the description adds value by instructing users to 'Describe the issue in terms of Pipeworx tools/packs' and providing examples of effective messages, which aids parameter usage beyond the schema's standard 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 explicitly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific scenarios (bug, feature, data_gap, praise) that clearly distinguish it from sibling tools, none of which are feedback-related.

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 clear when-to-use guidance: 'Use when a tool returns wrong/stale data (bug), when a tool you wish existed isn't in the catalog (feature/data_gap), or when something worked surprisingly well (praise).' It also states what not to do ('don't paste the end-user's prompt') and mentions rate limits and quota policy.

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

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

Annotations already indicate readOnlyHint=true, openWorldHint=true, and destructiveHint=false. The description adds valuable behavioral context: it explains the monotonicity rule, how the tool extracts and sorts dates/thresholds, and that it returns suggested trades. This goes beyond the annotations without contradicting them.

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 structured in two paragraphs: first explaining the concept, then the usage and output. It is front-loaded with the main purpose, but some explanatory text (e.g., 'when the SAME event...') could be trimmed slightly while retaining clarity. Overall, it is appropriately compact.

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 parameter and no output schema, the description fully covers what the tool does, how to use it, and what it returns (a list with market_a, market_b, gap_pp, suggested_trade). It explains the logic thoroughly, making it self-contained for a single-param 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% and the only parameter 'event' has a clear description. The tool description adds that the input can be a slug or full URL, but this is already implied by the schema description. Overall, the description does not significantly enhance 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's purpose: 'Find arbitrage opportunities within a Polymarket event by checking for monotonicity violations.' It uses a specific verb ('find') and resource ('arbitrage opportunities'), and explains the underlying logic, distinguishing it from sibling tools like 'polymarket_edges' by focusing on time/threshold 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 'Pass a Polymarket event slug or URL' and explains the tool's internal process (walking child markets, extracting dates/thresholds, sorting, reporting violations). This provides clear context for when to use it, though it lacks explicit when-not-to-use or alternative tool references.

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

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

Annotations already mark it as read-only and non-destructive. The description adds detailed internal behavior: scanning top markets, grouping by asset, fetching price history once, computing model probability, and ranking by edge. It also notes the V1 scope and model source, going beyond what annotations provide.

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

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

The description is informative but somewhat lengthy, including technical model details (lognormal from FRED + coinpaprika). It is front-loaded with the main action but could be more 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, the description explains return values (top N ranked by edge magnitude with suggested trade direction) and covers input, process, and purpose adequately for a moderate-complexity 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?

All three parameters have schema descriptions (100% coverage), so baseline is 3. The tool description restates concept of 'top N' and 'edge magnitude' but does not add significant new 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 tool scans Polymarket markets and returns edges where Pipeworx data disagrees with market price, with specific mention of crypto-price bets and the model used. However, it does not explicitly differentiate from the sibling 'polymarket_arbitrage' 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 use for discovering betting opportunities ('what should I bet on today'), but provides no explicit guidance on when not to use it or mention of alternatives.

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

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

Annotations provide readOnlyHint=true, and description adds scoping detail (anonymous IP, BYO key hash, account ID). No contradictions. Additional behavioral traits like no side effects are implied.

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

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

Three efficient sentences, front-loaded with action. No wasted words; every sentence adds clear 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 tool with one optional parameter and no output schema, the description fully covers behavior, scoping, and relationships. 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 has 100% description coverage for the single parameter. Description reinforces meaning: omitting key lists all keys. Adds valuable usage guidance beyond schema.

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

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

Clear statement of function: retrieve a saved value or list keys. Names paired tools (remember, forget) and distinguishes by explaining the two modes based on key presence.

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

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

Provides context for when to use (look up context stored earlier, avoid re-deriving) and scoping (by identifier). Lacks explicit not-to-use scenarios but sufficient for the tool's simplicity.

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 reveals the tool fans out to three external APIs (SEC, GDELT, USPTO), explains the `since` parameter syntax (ISO or relative), and lists output fields (changes, count, URIs). Annotations already indicate readOnlyHint=true, and the description adds context without contradiction.

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

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

The description is a single, dense paragraph that front-loads the tool's purpose and use cases. While it could be broken into bullet points for clarity, it remains efficient and informative without extraneous text.

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

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

The description covers the tool's input (type, value, since), internal behavior (fan-out to three sources), and output (structured changes, count, URIs) despite lacking an output schema. It does not mention pagination or limits, but for a straightforward query tool this is 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%, but the description adds value by explaining the `since` parameter accepts relative shorthand ('7d', '30d', '3m', '1y') and provides usage examples. It also clarifies `value` can be a ticker or CIK, supplementing the schema.

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

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

The description clearly states it returns recent changes for a company across multiple sources (SEC, GDELT, USPTO). It provides specific example queries and distinguishes from sibling tools like entity_profile by focusing on temporal updates.

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 gives explicit example queries ('use when user asks...') that cover common use cases. It does not explicitly mention when not to use or compare to alternatives, but the examples are sufficient for most scenarios.

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

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

Annotations provide readOnlyHint=true, which the description does not contradict. The description adds no further behavioral details (e.g., rate limits, pagination). With annotations covering safety, the description is adequate but minimal.

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

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

The description is extremely concise (4 words), avoiding unnecessary text. It is front-loaded but could benefit from a verb to improve clarity without significant bloat.

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 three optional parameters and no output schema, the description fails to explain what the tool returns or how to use filters. It leaves significant gaps for an agent to navigate effectively.

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?

All three parameters have descriptions in the input schema (100% coverage). The tool description adds no extra meaning beyond that, so it meets baseline without improvement.

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 'Recent recorded votes (divisions)' clearly indicates the tool provides recent parliamentary division votes. Combined with the name, it's specific and understandable, but lacks a verb and could be more explicit about 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?

No guidance on when to use this tool versus siblings like search_hansard or get_bill. It neither states context nor exclusions, leaving the agent to infer usage.

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

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

Discloses that the tool writes (consistent with readOnlyHint=false), describes scoping by identifier, and reveals persistence: permanent for authenticated users, 24-hour for anonymous sessions. No annotation 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?

Three sentences, no wasted words. First sentence defines purpose, second gives usage guidance, third adds behavioral details. Efficient and well-structured.

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

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

Complete for a two-parameter tool without output schema: covers purpose, usage triggers, behavioral details, and pairing with sibling tools. 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 already covers both parameters with examples and descriptions (100% coverage). The description adds context on storage as key-value pairs and scoping, providing extra value 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 saves data for reuse, specifies it stores key-value pairs, and distinguishes from siblings recall and forget by naming them and describing the pairing.

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

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

Explicitly tells when to use it: when discovering something worth carrying forward (e.g., ticker, address, preference). Also contrasts with recall and forget for retrieval/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?

Annotations indicate readOnlyHint=true, so description adds value by detailing return of IDs and pipeworx:// citation URIs. 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?

Concise 4-sentence description, front-loaded with purpose, then examples and usage. 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?

Though no output schema, description explains return values (IDs + citation URIs). Covers all essential aspects for this 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 covers both parameters fully. Description adds concrete examples (e.g., 'Apple' → AAPL) and explains expected input formats, enhancing meaning beyond schema.

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

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

Clearly states it looks up canonical/official identifiers for company/drug. Differentiates from siblings by noting it replaces multiple lookup calls and should be used before other tools.

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

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

Explicitly advises to use before other tools needing official identifiers. Provides examples and context. Could improve by mentioning when not to use, but sufficient.

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

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

Annotations already declare readOnlyHint=true, so the description only needs to add extra behavioral context. It does not contradict annotations. The description does not mention pagination or sorting behavior beyond what is in the schema, so it adds minimal transparency.

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

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

The description is a single, concise sentence without any extraneous words. Every word is useful.

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 8 parameters (all well-documented in schema) and no output schema, the description is somewhat brief but covers core functionality. It does not explain output format or that the query field searches both title and short title, but the schema already provides that detail. It is minimally complete.

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

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

Schema description coverage is 100%, so the schema already documents all parameters fully. The description lists a subset of parameters (title, session, stage, sponsoring member) but adds no new meaning beyond the schema. Baseline 3 is appropriate.

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

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

The description clearly states the action (search) and resource (bills), listing the key searchable fields (title, session, stage, sponsoring member). This distinguishes it from siblings like get_bill (single bill retrieval) and search_hansard (different content type). However, it could be more explicit about returning a list of bills.

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 bills but provides no explicit guidance on when to use this tool versus alternatives. Siblings include get_bill for a specific bill and search_hansard for hansard text; the description does not compare or exclude these.

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?

While the annotation readOnlyHint=true indicates a safe read operation, the description adds no additional behavioral context such as pagination, rate limits, or result format. For a search tool, more transparency would be beneficial.

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 with no unnecessary words. It is front-loaded with the key action and resource, but could be slightly more structured to highlight key 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?

Without an output schema, the description should explain what the tool returns (e.g., list of contributions). It does not. Also, with 7 parameters and a search functionality, more context on query logic or filtering behavior 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?

The input schema has 100% description coverage for all 7 parameters, so the schema itself is adequate. The description does not add any extra meaning beyond what is already in the schema.

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

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

The description clearly states the action (Search), resource (debate contributions), and domain (Hansard). It is specific and distinguishes from sibling search tools like search_bills and search_members.

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

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

No explicit guidance on when to use this tool versus alternatives, nor any conditions or exclusions. The description does not help an agent decide between this and other search tools.

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

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

Annotations already mark readOnlyHint=true, so safety is clear. Description adds that the tool searches across multiple fields, but doesn't elaborate on pagination or defaults beyond schema. Consistent and generally 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?

Single sentence, highly efficient, zero wasted words. Front-loaded with verb 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?

With 7 parameters and 100% schema coverage, the description is brief but covers the core search capabilities. Does not mention defaults (is_current) or pagination, but schema already provides that. Adequate for a search 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 baseline is 3. Description mentions key parameters but adds no extra meaning beyond the schema (e.g., name search behavior). Meets minimum but doesn't enhance.

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

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

Clearly states the tool searches MPs and Lords, specifying exact search fields (name, party, location, house). Distinguishes from siblings like get_member and search_bills.

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?

Doesn't explicitly state when to use this tool vs alternatives (e.g., get_member for specific IDs). Implies usage for searching, but no when-not-to or alternative 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 readOnlyHint=true, which aligns with the description. The description adds transparency about output (verdict, structured form, actual value with citation, percent delta) and that it replaces 4-6 sequential calls, providing useful context beyond annotations.

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

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

The description is concise (~120 words), well-structured: starts with purpose, then usage guidance, scope, output summary, and value proposition. No redundant information, front-loaded with key verbs and examples.

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

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

Despite no output schema, the description fully explains the output fields (verdict, structured form, actual value, percent delta). It covers input, usage, and output comprehensively for a complex fact-checking 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?

Only one parameter 'claim' with a description. Schema coverage is 100%, so baseline is 3. The description adds value by providing example claims, which helps the agent understand the expected format better than the schema alone.

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

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

The description clearly states the tool's purpose: fact-check a natural-language factual claim against authoritative sources. It specifies the domain (company-financial claims) and provides example claims. It is distinct from sibling tools which focus on parliamentary data.

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

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

The description explicitly says 'Use when an agent needs to check whether something a user said is true' and gives example phrasings. It also notes the scope (v1 supports company-financial claims). However, it does not explicitly state when not to use it (e.g., for non-financial claims), but overall guidance is clear.

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