Goproxy

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

The description reveals that the tool routes questions to sub-tools, fills arguments automatically, and returns structured answers with stable citation URIs. Annotations already indicate read-only and non-destructive, so the description adds context about internal routing and output format, but could mention failure modes or coverage limits.

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

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

The description is well-structured, starting with a strong usage directive and then listing domains, triggers, and examples. Every sentence adds value, and the length is justified by the complexity of the tool's scope.

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, the description covers the intended use, return format (structured answer with citations), and prioritization over web search. It lacks details on error handling or missing tools, but the annotations (openWorldHint) and examples provide sufficient context for an agent.

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

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

With only one parameter and 100% schema coverage, the description greatly enhances semantics by providing extensive examples of valid questions, domain coverage, and usage triggers, far surpassing the simple schema description.

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

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

The description clearly states it is a routing tool for factual queries across 2,520 tools, covering a wide range of data types. It distinguishes itself from siblings by explicitly preferring over web search and detailing specific domains and question patterns.

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 when-to-use guidance: prefer over web search for factual questions about real-world data. It lists example queries and patterns like 'what is', 'look up', 'find', making the decision clear. It does not explicitly state when not to use, but the positive 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 declare readOnlyHint (safe read), openWorldHint (external data), destructiveHint=false. Description adds behavior: classifies bet, fans out to packs, returns evidence and 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?

Description is slightly long but each sentence adds value. Front-loaded with purpose and input examples. Could be slightly more concise, but structure is 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?

No output schema, but description mentions evidence packet and market-vs-model comparison. Covers complexity: classification, fan-out, edge detection. Nearly complete for a research tool.

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

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

Schema coverage is 100% with descriptions. The description adds context: 'market' accepts slug, URL, or question text; 'depth' explains quick vs thorough. Provides 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 it researches a Polymarket bet by pulling Pipeworx data, specifying the action, resource, and input types (slug, URL, question). It distinguishes itself from siblings like polymarket_arbitrage by focusing on data research.

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 explicit use cases: 'should I bet on X?', 'what does the data say about this Polymarket market?', 'is there edge in this bet?'. Does not explicitly state when not to use or list alternatives, but the guidance is clear.

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

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

Annotations already indicate read-only, open-world, non-destructive. Description adds data sources (SEC EDGAR/XBRL, FAERS, FDA) and return type (paired data + URIs), enriching transparency 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?

Single, well-organized paragraph: purpose first, usage triggers, per-type details, return info, and benefit. Every sentence serves a purpose with no redundancy.

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

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

Covers all essential aspects: what it does, when to use, parameter specification, data sources, return format, and efficiency gain. No output schema, but return type is briefly described.

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

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

Input schema covers both parameters with descriptions; schema coverage is 100%. Description adds concrete examples (tickers like AAPL, MSFT; drug names like ozempic, mounjaro) and clarifies data per type, adding 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 'Compare 2–5 companies (or drugs) side by side in one call', specifying the action, entity types, and scope. It differentiates from sibling tools like entity_profile by emphasizing batch comparison and replacing sequential calls.

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

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

Explicitly lists user utterances that trigger use ('compare X and Y', 'X vs Y', etc.) and contrasts with alternatives by noting it replaces 8–15 sequential agent calls. Provides clear when-to-use 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 already indicate readOnlyHint=true and destructiveHint=false, so the description adds context about returning 'the top-N most relevant tools with names + descriptions.' This complements the annotations without contradicting them. No additional behavioral traits are needed.

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

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

The description is front-loaded with the primary purpose but includes an extensive list of examples that, while helpful, could be trimmed for brevity. It remains clear and logically structured, though slightly longer than necessary.

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 is simple (no output schema, no nested objects). The description covers its purpose, usage guidance, and return values. It does not mention server scope (only tools on this server) or potential limitations, but annotations and schema adequately address missing details.

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 both parameters are fully described. The description adds context like 'natural language description' for query and 'top-N most relevant' for limit, but these are already implied or stated in the schema. No significant added 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's purpose: 'Find tools by describing the data or task.' It uses a specific verb (find/discover) and resource (tools), and distinguishes itself from sibling tools by being the only one that searches across all tools. Examples like 'SEC filings, financials, FDA drugs' further clarify its scope.

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

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

The description explicitly advises when to use this tool: 'Use when you need to browse, search, look up, or discover what tools exist' and 'Call this FIRST when you have many tools available.' It also contrasts with using a single tool directly, providing clear context for selection.

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

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

Annotations already define readOnlyHint=true and destructiveHint=false, so the description's read-only nature is consistent. It adds behavioral context by listing the types of data returned (SEC filings, fundamentals, patents, news, LEI) and mentions citation URIs. However, it does not discuss potential data staleness or rate limits, which could reduce transparency slightly.

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 dense but well-structured, front-loading the main purpose and use cases. While it is slightly long, every sentence adds value, and it avoids repetition of schema information.

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

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

Given the tool's complexity (aggregates multiple data sources) and lack of output schema, the description thoroughly covers what to expect in the return, including specific data types and the fact that citations are provided. It also mentions a prerequisite (resolve_entity for names), making it complete for the agent's 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 description coverage is 100%, but the description adds significant meaning: 'type' gets context that only 'company' is supported with future plans for person/place; 'value' clarifies it accepts ticker or CIK and explicitly says names not supported, guiding correct usage.

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

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

The description uses a specific verb ('Get everything about a company in one call') and resource ('company'), and explicitly lists use cases ('tell me about X', 'research Microsoft') that differentiate it from siblings like 'resolve_entity' or 'compare_entities' which serve different purposes.

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

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

It explicitly states when to use the tool (when a user asks for company profile or research) and when not to use it (if only a name is available, instructing to use 'resolve_entity' first). This provides clear guidance on alternatives.

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

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

Annotations already indicate destructiveHint=true. Description adds context about clearing sensitive data and use cases, but doesn't reveal additional behavioral traits beyond the annotation.

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 action, no redundant words. Efficiently communicates 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?

For a simple deletion tool with one required param and no output schema, the description covers core function and usage context. Minor omission: no mention of whether deletion is permanent or if feedback is 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?

Schema covers 100% of parameters with description 'Memory key to delete'. Description doesn't add new meaning or constraints, meeting baseline expectation.

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 uses specific verb 'Delete' and resource 'memory by key', clearly distinguishing from siblings like remember and recall. Makes the tool's function 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 states when to use: when context is stale, task done, or clearing sensitive data. Advises pairing with remember and recall, providing alternative tool 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 already declare readOnlyHint=true and destructiveHint=false, clearly indicating safe read-only behavior. The description adds no further behavioral details (e.g., return format, data source), but the annotations cover the key safety traits. However, openWorldHint is not explained, which could confuse an agent relying solely on description.

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

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

The description is extremely short (2 words), which might seem concise, but it sacrifices essential information. It is not well-structured because it fails to provide a verb or context. Every word should earn its place, and here it does not.

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 of the tool (2 params, no output schema), the description should still indicate what is returned (e.g., the raw file contents, parsed structure). Without an output schema, this gap is significant. The description is incomplete for an agent to understand the tool's full effect.

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

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

With 0% schema description coverage, the description adds zero meaning to the parameters. It does not explain that 'module_path' is likely a Go module path or that 'version' is optional. The agent must guess from parameter names alone.

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

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

The description 'go.mod content' is minimal and lacks a verb, making it unclear whether the tool retrieves, parses, or modifies go.mod. It only identifies the resource, which is almost redundant given the tool name. No differentiation from siblings like 'module' or 'version_info'.

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 prerequisites, context, or restrictions, leaving the agent to infer usage from the name and schema 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 indicate readOnlyHint=true but the description adds no behavioral details. It fails to explain what 'most recent version' entails or any other 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 extremely short but lacks necessary information. It is under-specified rather than efficiently 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 should clarify return value. 'Most recent version + metadata' is insufficient. Parameter role is also unaddressed.

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 0% and the description does not explain the 'module_path' parameter. The agent has no information about what value to provide.

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 'Most recent version + metadata' is vague and lacks a specific verb. It does not clarify whether the tool retrieves a module, lists versions, or something else.

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 compared to siblings like 'versions' or 'version_info'. The description does not differentiate or provide context.

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 adds behavioral details beyond annotations: rate-limited, free, no quota impact, team reads digests daily, signal affects roadmap. Annotations (readOnlyHint=false, destructiveHint=false) are consistent. 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 concise but comprehensive, with the main purpose first, then categories, usage instructions, and constraints. Every sentence adds value; no fluff.

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

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

Given 3 parameters (one with enums, one nested), no output schema, the description covers all necessary information: what to input, how to format, constraints, and what happens to the feedback. It's complete for a simple feedback tool.

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

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

Schema coverage is 100% with descriptions for each parameter. The description adds usage guidance (e.g., 'Be specific - which tool, what error') and summarizes enum values, 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's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It specifies four categories (bug, feature, data_gap, praise), distinguishing it from sibling tools like ask_pipeworx (questions) and discover_tools (exploration).

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 defines when to use each type (bug, feature, etc.), instructs not to paste user prompts, mentions rate limits (5 per identifier per day), and notes it's free and doesn't count against quota. This provides clear guidance on when and how to use the tool.

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

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

Annotations already mark it as readOnly and open world. The description adds that it returns ranked opportunities with reasoning and explains internal logic (monotonicity checks). No contradictions, but could mention data freshness or limits.

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

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

The description is well-structured with bold keywords and clear sections, but is slightly verbose. Every sentence adds value, but could be more 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?

No output schema, but description mentions return format (ranked opportunities with reasoning). It explains the concept of monotonicity violations sufficiently. Lacks error handling or edge cases, but adequate for a read 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 description adds meaningful context: examples for event, explanation of topic mode, and how cross-event grouping works. Adds 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 finds arbitrage opportunities via monotonicity violations, distinguishes two modes (event vs topic), and explains the cross-event advantage. It differentiates from siblings like 'polymarket_edges' and general 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 explains two modes, when to use each (e.g., topic mode for cross-event cases), and gives concrete examples (slugs, seed questions). It provides clear context for selection.

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

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

Despite annotations indicating read-only and non-destructive behavior, the description adds rich algorithmic details: uses lognormal model from FRED and live coinpaprika price, scans top markets, groups by asset, fetches price history once, computes model probability, ranks by |edge|, and returns top N. No contradiction with annotations.

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

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

Description is moderately long but every sentence adds value: purpose, method, output, and use case. It is well-structured and front-loaded with the core function. Not overly verbose, but could be slightly 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?

No output schema exists, but description describes return values as 'top N ranked by edge magnitude with suggested trade direction.' Covers algorithm, inputs, and output type. Lacks exact field names, but sufficient for agent to infer. For a tool with three parameters and clear behavior, it 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 coverage is 100% with clear descriptions for all three parameters. The description adds minimal new information beyond what the schema provides (e.g., mentions 'top N' aligning with limit). Baseline 3 is appropriate as the description does not significantly enhance parameter understanding.

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

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

Description clearly states action (scan and return markets where Pipeworx data disagrees), resource (highest-volume Polymarket markets), and output (top N ranked by edge magnitude). It distinguishes itself from siblings like polymarket_arbitrage by focusing on edge detection for betting opportunities.

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 it is built for the 'what should I bet on today' question, indicating when to use. However, it does not explicitly mention when not to use or compare with sibling tools.

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

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

Adds value beyond annotations by stating scoping to identifier (anonymous IP, etc.) and that it is non-destructive. 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?

Three sentences, front-loaded with action, each sentence provides essential information without redundancy.

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

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

Covers return type (value or list), scoping, pairing with siblings, and parameter usage. Despite no output schema, description is complete for a simple one-parameter 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 schema description already explains the key parameter adequately. Description adds no new parameter-specific meaning beyond repetition.

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

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

Description clearly states the verb 'retrieve' and resource 'saved value', and distinguishes from sibling tools remember and forget by mentioning them.

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

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

Explicitly says when to use (look up context stored earlier) and when to omit key (list all). Mentions pairing with remember/forget but lacks explicit when-not-to-use or alternatives beyond those.

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

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false. The description adds significant behavioral context: it fans out to three specific sources (SEC EDGAR, GDELT, USPTO) in parallel, explains the 'since' parameter formats (ISO or relative), and describes the return structure (structured changes + total_changes count + 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 a single dense paragraph with no wasted words. It uses bold for key terms, lists example queries, and covers purpose, inputs, behavior, and output in a concise manner. Every sentence earns its place.

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

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

Given no output schema, the description compensates by summarizing the return fields (structured changes, total_changes count, citation URIs). It covers purpose, inputs, behavior (three sources), and output. However, it could mention pagination or limits, but that is not essential for the agent to use the tool correctly.

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

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

Schema description coverage is 100%, so baseline is 3. The description adds value by explaining the 'since' parameter formats in detail (ISO date or relative shorthand), giving examples for 'value' (ticker or CIK), and recommending '30d' or '1m' for typical monitoring. This goes beyond the schema's minimal description.

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

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

The description clearly states 'What's new with a company in the last N days/months?' and provides specific example queries. It identifies the resource (company), the action (get recent changes), and the time window. It also mentions the sources (SEC, GDELT, USPTO), making it distinct from siblings like 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?

The description gives explicit use cases ('use when a user asks...') and lists example phrasings. It also mentions fan-out to multiple sources. However, it does not explicitly exclude scenarios where alternative tools (e.g., entity_profile) would be more appropriate, which slightly reduces guidance completeness.

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 a write operation (readOnlyHint false, destructiveHint false). The description adds meaningful context: key-value scope by identifier, persistent for authenticated users, 24-hour retention for anonymous sessions. 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 purpose, and efficiently covers usage, behavior, and sibling tools. Every sentence adds value with no redundancy.

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

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

For a simple 2-parameter tool with full schema coverage and no output schema, the description is complete: it explains when to use, how storage works, persistence rules, and links to retrieval/deletion tools. 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?

Schema coverage is 100% with descriptions for both parameters. The description reinforces the key-value nature and provides examples but adds no new information about parameter constraints or formats 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 uses specific verb-resource phrasing ('Save data... to reuse later') and provides concrete examples of use cases (resolved ticker, target address, user preference). It clearly distinguishes from sibling tools recall and forget by naming them as partners.

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 ('when you discover something worth carrying forward') and pairs with recall and forget for retrieval/deletion, giving clear context. It also notes persistence differences between authenticated and anonymous users.

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, so the description adds value by specifying the output includes 'pipeworx:// citation URIs' and listing the ID types. However, it does not discuss any edge cases or limitations (e.g., what happens if the name is ambiguous).

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 yet comprehensive, packed into a single focused paragraph. Every sentence adds value: purpose, usage context, examples, return format, and relationship to other tools. No unnecessary fluff.

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

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

Despite lacking an output schema, the description fully explains what the tool returns (IDs + citation URIs) and provides multiple examples. Given the tool's low complexity (2 params), the description covers all necessary context for correct usage.

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

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

Schema coverage is 100%, so the description's examples (e.g., 'Apple → AAPL / CIK 0000320193') add meaningful context beyond the schema definitions. The baseline is 3, and the examples provide extra clarity for parameter values.

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: 'Look up the canonical/official identifier for a company or drug.' It names specific ID systems (CIK, ticker, RxCUI, LEI) and provides concrete examples, making it easily distinguishable from sibling tools like bet_research 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?

The description provides explicit guidance on when to use: 'Use when a user mentions a name and you need the [IDs]...' and advises to use it 'BEFORE calling other tools that need official identifiers.' It also notes it 'Replaces 2–3 lookup calls,' which helps the agent understand its value relative to alternatives.

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

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

Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds value by detailing return values (verdict types, structured form, citation, percent delta) and noting that it replaces multiple calls. No contradictions 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 concise with five sentences, each serving a distinct purpose: purpose, usage guidelines, scope/limitations, return details, and efficiency benefit. No redundant or unnecessary information.

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

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

Given that there is no output schema, the description adequately covers inputs, behavior, and outputs (verdict types). It provides enough context for an agent to understand the tool's capabilities and limitations, though more detail on output format could be beneficial.

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%, providing a decent description of the 'claim' parameter. The tool description adds example claims and context about natural-language format, enhancing 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's purpose: fact-checking, verifying, validating, or confirming/refuting natural-language factual claims against authoritative sources. It uses specific verbs and resources, and distinguishes itself from sibling tools like ask_pipeworx or compare_entities by focusing on claim validation.

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

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

The description explicitly says when to use: 'when an agent needs to check whether something a user said is true', with example queries. It also specifies scope (v1 supports company-financial claims via SEC EDGAR + XBRL), implying limitations. However, it does not explicitly mention when not to use or alternative tools for non-financial claims.

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

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

Annotations already declare readOnlyHint=true and destructiveHint=false; description adds no behavioral context such as whether it returns a single record or list, or any side effects.

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

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

Extremely short (2 words) but under-specified; lacks structure. Not conciseness in the helpful sense.

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

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

No output schema, 0% param coverage, 2-word description. Fails to inform agent about return format or parameter constraints.

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 0% and description provides no explanation of the two required parameters (module_path, version). Agent has no clue what values are valid.

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 'Version metadata' is vague; it mentions 'metadata' but does not specify what resource (e.g., module version) or how it differs from sibling tool 'versions' which likely lists versions.

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 vs alternatives like 'versions'. No context on prerequisites or expected input format.

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 read-only and non-destructive behavior. The description adds no additional behavioral details (e.g., response format, pagination).

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

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

Extremely short but not effectively concise; it omits critical context. Every sentence should add value, but this single sentence is insufficient.

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 one required parameter and no output schema, the description should clarify what is returned and how the parameter is used. It fails to provide a complete picture.

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%. The description does not explain the 'module_path' parameter, leaving the agent to guess its format or purpose.

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

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

Description states 'List available versions' – a clear verb and resource, but fails to specify what kind of versions (e.g., module versions). This vagueness makes it hard to distinguish from the sibling 'version_info' 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?

No guidance on when to use this tool versus alternatives like 'version_info'. No context for prerequisites or typical use cases.

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