Gitlab

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It discloses that the tool selects the best data source, fills arguments, and returns results, indicating automated orchestration. This is sufficient for a high-level question-answering tool, though it does not detail specific behaviors like error handling or latency.

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 front-loaded with the purpose. Each sentence adds value: purpose, behavior, and examples. Slightly verbose due to examples, but effective.

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

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

Given the tool's simplicity (one param, no output schema, no nested objects), the description is complete enough. It explains the tool's role as an orchestrator and provides examples. No output schema exists, but description does not need to explain return values as it is a generic Q&A tool.

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

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

Schema coverage is 100%, so the schema already documents the single parameter. The description adds context that the question should be in natural language and provides examples, but does not add structural details 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 tool accepts a plain English question and returns an answer from the best data source. It explicitly distinguishes itself from other tools by acting as an orchestrator that selects tools and fills arguments, contrasting with sibling tools that perform specific tasks.

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

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

The description explains when to use this tool: when you have a natural language question and want the system to handle tool selection. It provides examples to illustrate usage. However, it does not explicitly state when not to use it or mention alternatives, but the examples and context imply it is the default for questions.

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 behavioral context: it resolves markets, classifies bet types, fans out to appropriate packs, and returns an evidence packet with a market-vs-model comparison. This goes 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 moderately concise: 4 sentences covering purpose, process, usage examples, and value proposition. Each sentence adds unique information, though it could be slightly more compact. It is front-loaded with the most critical 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?

With no output schema, the description adequately explains the return format (evidence packet plus market-vs-model comparison). It also outlines internal steps (resolve, classify, fan out). For a 2-parameter tool with simple inputs, this provides sufficient completeness.

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%, describing both parameters minimally. The description enriches this by clarifying that the 'market' parameter accepts a slug, URL, or question text, and that 'depth' has 'quick' and 'thorough' options. It adds meaningful context beyond the schema.

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

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

The description clearly states the tool's purpose: research a Polymarket bet by pulling relevant Pipeworx data. It specifies the action (Research), the resource (Polymarket bet via Pipeworx), and differentiates from siblings by emphasizing it is the core demo product that converts better than manual discovery.

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

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

The description explicitly defines when to use the tool with example questions like 'should I bet on X?' and 'what does the data say about this Polymarket market?'. It implies superiority over alternative approaches but does not explicitly state when not to use it.

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

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

No annotations, but description discloses data sources (SEC EDGAR, FDA) and return type (paired data + URIs). Could mention that it is a read-only operation explicitly.

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 succinct sentences with front-loaded purpose. 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?

Covers key aspects: what it does, parameters, data sources, and return value. Lacks exact output format but acceptable for complexity level.

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

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

Schema coverage is 100% (baseline 3). Description adds meaningful value by explaining data points per type and value formats beyond the minimal 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?

Clearly states it compares 2-5 entities side by side with specific data points for each type. Differentiates from siblings by being the only comparison 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?

Indicates efficiency ('replaces 8-15 sequential calls') but lacks explicit when-not-to-use or alternatives. Adequate given sibling 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?

Discloses that it returns the most relevant tools with names and descriptions, and uses natural language input. However, no annotations are provided; description covers behavioral traits well, but could mention if it only returns top matches or has 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?

Two sentences, front-loaded with key action and result, no wasted words.

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

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

Given low complexity (2 params, no nested objects, no output schema), the description is mostly complete. Could mention if results are ranked by relevance, but sufficient for typical use.

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

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

Schema coverage is 100%, so baseline is 3. Description adds context on natural language usage but does not add meaning beyond schema for 'limit' or 'query'.

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 action (search) and resource (tool catalog), and distinguishes itself from siblings by specifying it is for finding tools among 500+ options.

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

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

Explicitly instructs to call this FIRST when many tools are available, and provides context on when to use it (finding right tools) versus 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?

No annotations provided, so description bears full burden. Discloses return format (citation URIs), bundling behavior, and what data is included. Lacks details on rate limits or performance impact, but overall transparent given context.

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

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

Description is concise, well-structured, and front-loaded with key information. Uses bullet-style enumeration for included data and includes concrete examples, making it 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?

Tool has only 2 required parameters with full schema coverage. Description covers purpose, usage, return format, and alternatives. No output schema needed as behavior is clearly communicated.

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% coverage with descriptions. Description adds value by explaining that 'value' accepts ticker or CIK and that names are unsupported, also clarifying the 'type' enum is limited to 'company'.

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 specifies that the tool returns a full entity profile across multiple Pipeworx packs, listing specific data sources (SEC filings, XBRL, patents, news, LEI) and stating it replaces 10-15 sequential calls. It also distinguishes from sibling tool usa_recipient_profile for federal contracts.

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

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

Provides explicit guidance: use for comprehensive entity profiles, for federal contracts call usa_recipient_profile instead, and for names use resolve_entity first. Clearly states alternatives and prerequisites.

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

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

With no annotations, the description carries full burden. It states it deletes a memory by key, implying irreversibility, but doesn't specify if it's idempotent, what happens if key doesn't exist, or if there are any confirmations. 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?

Very concise, one short sentence. No unnecessary words. However, could include a bit more context without losing conciseness.

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 (1 param, no output schema, no annotations), the description is minimally complete. It states the action and the required parameter. No extra context about return values or side effects is provided, but the tool is simple.

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 no additional meaning beyond the schema's description of 'Memory key to delete'. Baseline 3 is appropriate since schema already documents the 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 uses a clear verb 'Delete' and a specific resource 'a stored memory by key'. It distinguishes from sibling tools like 'remember' and 'recall' by indicating a write/destructive action, but does not explicitly differentiate from all siblings.

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 'remember' or 'recall'. There is no mention of prerequisites, side effects, or when not to use it.

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

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

The description discloses that content is base64-encoded and lists returned fields, but with no annotations present, it does not cover potential side effects, authentication requirements (beyond the API key parameter), or rate limits. A 3 is appropriate given the absence of 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 and efficiently states purpose and return values. Slight room for improvement by front-loading the base64 decoding hint, but still 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 that there is no output schema, the description partially fills the gap by listing return fields, but it omits behavioral details like pagination or error cases. It is adequate but not thorough.

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 no parameter-specific context beyond what the schema already provides. Baseline 3 is correct since the schema is self-sufficient.

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-resource pair ('Get a file from a GitLab repository') and enumerates return values (content, name, size, encoding), clearly distinguishing it from siblings like gitlab_get_project or gitlab_list_projects.

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 (e.g., gitlab_get_project for project metadata), nor are there any when-not-to-use or prerequisite instructions.

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

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

Annotations are empty, so description carries full burden. It discloses that the tool returns full project details but does not mention side effects, authentication requirements (beyond the API key param), or rate limits. Adequate but not comprehensive.

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

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

Two sentences, efficient and front-loaded with the core action. No unnecessary words.

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

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

Given the tool is a simple retrieval with no output schema, the description covers the main purpose and return types. However, it could mention that the tool is read-only, which is implied but not stated.

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 mentions 'ID or URL-encoded path' but does not add significant meaning beyond the schema's description of the id parameter. No additional value for the _apiKey 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 a single GitLab project by ID or URL-encoded path, and lists the types of details returned. This is specific and distinct from sibling tools like gitlab_list_projects.

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

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

The description explains what the tool does but does not provide guidance on when to use it versus alternatives like gitlab_list_projects. No explicit when-not or exclusion criteria are given.

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 notes that it returns specific fields (IID, title, state, labels, assignee, URL), but does not mention pagination behavior, rate limits, or that the API requires authentication (though _apiKey parameter covers that). No annotations are provided, so description carries full burden, but it is adequate for a read-only list 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 concise (two sentences) and front-loaded with the main action. It provides useful information about returned fields without unnecessary detail.

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 list tool with no output schema, the description is fairly complete. It explains what the tool does and what it returns. However, it could mention pagination (per_page parameter) and default filtering by state (opened), but those are in the schema.

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

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

The input schema has 100% coverage, so the description's lack of parameter details is compensated. The description lists returned fields but not parameter specifics, which is acceptable given the schema richness.

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 ('List issues') and the resource ('in a GitLab project'), and lists the returned fields (IID, title, state, labels, assignee, URL). This distinguishes it from siblings like 'gitlab_list_mrs' which list merge requests, not issues.

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

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

The description provides no guidance on when to use this tool versus alternatives (e.g., when to use search vs listing all, or when filtering by state). It does not mention any prerequisites like needing to know the project ID beforehand.

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

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

No annotations provided, so description carries burden. It mentions returned fields but does not disclose pagination behavior beyond schema (per_page), rate limits, or authentication details. Adequate but not rich.

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

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

Two sentences, concise and front-loaded with action and resource. No wasted words.

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

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

For a list tool with no output schema, description provides key fields returned. However, lacks details like sorting, filtering beyond state, and default behavior. Adequate for simple use.

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

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

Schema description coverage is 100%, so parameters are well-documented in schema. Description adds value by listing returned fields but no additional parameter details. Baseline 3 is appropriate.

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

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

Description uses specific verb 'List' and resource 'merge requests in a GitLab project', and lists the returned fields, distinguishing it from siblings like gitlab_list_issues and gitlab_list_projects.

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 vs alternatives, though the description implies listing MRs with filtering by state. No mention of when not to use it.

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

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

Annotations are empty, so the description must cover behavioral traits. It states the tool lists projects accessible to the user, which implies it is a read-only operation. However, it does not disclose pagination behavior beyond the per_page parameter, rate limits, or the fact that it uses the authenticated user's token. The description adds value by listing returned fields, but could be more explicit about the readonly nature.

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

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

The description is two sentences, front-loaded with the primary action and resource. It lists key return fields efficiently. No unnecessary words.

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

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

Given the tool has 4 parameters with full schema coverage, and no output schema, the description is adequate but could benefit from stating that results are paginated and that the user must have appropriate GitLab access. It lists returned fields, which is helpful.

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

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

Schema description coverage is 100%, so the schema already documents all parameters. The description does not add any additional parameter meaning beyond what is in the schema. Baseline score of 3 is appropriate.

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

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

The description clearly states the verb 'List' and the resource 'GitLab projects', and mentions the user context ('accessible to the authenticated user'). It distinguishes from siblings like 'gitlab_get_project' by indicating it returns a list, but does not explicitly differentiate from 'gitlab_list_issues' or 'gitlab_list_mrs', which are separate resource types.

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

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

The description implies usage when needing to list projects for the authenticated user, but provides no guidance on when not to use it or alternatives. No explicit comparison with siblings is given.

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

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

No annotations provided, so the description fully bears the burden. It discloses the rate limit (5/day), that it is free, and instructs to avoid including verbatim prompts. This gives the agent sufficient behavioral context for a simple feedback tool. Could mention if feedback is anonymous, but not necessary.

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 plus a rate-limit note. Front-loaded with purpose, then use cases, then constraints. Every sentence adds value with no wasted words.

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

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

For a feedback tool with no output schema, the description covers purpose, usage guidelines, parameter semantics, and behavioral constraints completely. The optional context parameter is explained. Nothing essential is missing.

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 detailed descriptions for each parameter (type enum with meanings, message with length limit, context optional). The description adds extra semantic guidance: 'Describe what you tried in terms of Pipeworx tools/data' and the rate limit, which aids proper invocation but does not repeat schema details.

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

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

The description clearly states 'Send feedback to the Pipeworx team' and enumerates specific use cases: bug reports, feature requests, missing data, or praise. This distinguishes it from all sibling tools, which are query or data manipulation tools.

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

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

The description specifies when to use (bug reports, feature requests, etc.) and provides important constraints: 'Describe what you tried in terms of Pipeworx tools/data — do not include the end-user's prompt verbatim' and 'Rate-limited to 5 messages per identifier per day.' However, it does not explicitly state when not to use this tool, but given no alternative feedback tool exists, this is adequate.

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. Description adds rich behavioral detail: walks child markets, groups related markets, checks monotonicity, returns ranked opportunities with reasoning. 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?

Single well-structured paragraph. Front-loaded with purpose, then logically explains two modes. Every sentence adds value—no fluff. Appropriate length for the complexity.

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

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

No output schema, but description states it returns 'ranked opportunities with suggested trade direction + reasoning'—sufficient. Clearly explains tool mechanics given two params and no nested objects. Fully informs agent for correct invocation.

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

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

Schema coverage is 100% with individual param descriptions. The tool description adds contextual meaning: explains that 'event' is slug/URL for single-event mode, and 'topic' is a seed question for cross-event search. Includes concrete examples, going 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 finds arbitrage opportunities via monotonicity violations on Polymarket. Describes two distinct modes (event vs topic) which differentiates the tool's scope and helps select correctly among siblings.

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 explains when to use each mode, provides example inputs, and justifies why cross-event mode is needed for cutoffs listed as separate events. Offers clear context for choosing mode.

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?

Beyond annotations (readOnlyHint, openWorldHint), the description adds detailed behavioral context: covers crypto-price bets, uses lognormal model from FRED and coinpaprika, scans top markets, groups by asset, fetches price history once, computes probabilities, and ranks by edge. 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 a concise single paragraph of five sentences, front-loaded with the main purpose and followed by necessary details. Every sentence adds value without redundancy.

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

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

Given the tool's complexity (model-based analysis, external sources), the description covers methodology, scope (V1), and output format. No output schema is present, but the description adequately states returns. All critical aspects are 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?

Schema description coverage is 100%, so baseline is 3. The description adds minimal extra meaning beyond the schema, such as explaining 'edge' concept and defaults, but 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?

The description clearly states the verb 'Scan' and 'return' with the resource 'highest-volume Polymarket markets'. It specifies the unique function of identifying where Pipeworx data disagrees with market price, distinguishing it from siblings like polymarket_arbitrage and bet_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?

The description explicitly identifies the use case: 'Built for the "what should I bet on today" question' and mentions avoiding manual paging. It provides clear context but does not explicitly state when not to use or list 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?

No annotations are provided, so the description must cover behavioral traits. It indicates read-only behavior and session persistence, but doesn't mention whether memories persist across sessions, limits, or thread safety.

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, no wasted words, front-loaded with the core action, and immediately 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 low complexity (one param, no output schema), the description covers retrieval and listing. It could mention what happens if key doesn't exist, but overall adequate.

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

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

Schema coverage is 100% with one parameter described. The description adds that omitting key lists all keys, which is not in schema, but adds no extra semantics beyond the schema.

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

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

The description clearly states the verb 'retrieve' and the resource 'stored memory', with explicit behavior for key omission. It distinguishes itself from sibling 'remember' and 'forget' by focusing on 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?

It explicitly says to use when retrieving saved context and omits mention of alternatives. However, it does not explicitly state when not to use it or compare to siblings like 'discover_tools'.

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

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

No annotations provided, so description carries full burden. It discloses parallel fan-out to SEC EDGAR, GDELT, USPTO, accepted date formats, and return structure including URIs. Does not cover rate limits or auth, but core behavior is well-described.

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 and front-loaded with purpose, then details. Every sentence adds value without redundancy.

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

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

Given complexity (multiple sources, date formats, return structure), description is comprehensive. No output schema, but return format is described. Covers all necessary aspects for an 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 coverage is 100%, and description adds significant value: explains the 'type' parameter is only 'company', lists supported date formats with examples, and clarifies 'value' can be ticker or CIK. This goes 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 it retrieves 'What's new about an entity since a given point in time.' and specifies the entity type 'company' with fan-out to multiple sources, distinguishing it from sibling tools like entity_profile 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?

The description explicitly recommends use for 'brief me on what happened with X' or change-monitoring workflows. It does not mention when not to use it or alternatives, but context is clear enough.

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

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

With no annotations, description carries full burden. Discloses persistence behavior (authenticated persistent, anonymous 24h). Does not mention idempotency or overwrite behavior on duplicate keys.

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

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

Three sentences, each adds value: purpose, usage context, persistence details. 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?

Simple tool with two string params and no output schema. Description is complete for typical use. Could mention that values are mutable or if there is a size limit.

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

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

Schema coverage is 100% with good descriptions for both parameters. Description adds usage examples for keys (e.g., subject_property) and clarifies value can be any text. Adds 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 'store a key-value pair' with specific usage context (session memory, intermediate findings, user preferences, context across tool calls). Distinguishes from sibling 'recall' and 'forget' by focusing on storage.

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

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

Provides explicit usage contexts (save findings, preferences, context). Mentions persistence differences for authenticated vs anonymous users. Does not explicitly say when not to use it, but sibling context makes it clear.

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

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

With no annotations, the description carries full burden. It clearly states the output includes ticker, CIK, company name, and pipeworx:// URIs. It also implies safe read-only behavior. However, it doesn't discuss error handling or behavior on unresolvable entities.

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 extremely concise: two sentences that cover purpose, version, input types, examples, output, and benefits. Every sentence contributes meaning without redundancy.

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

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

Given the tool's simplicity (2 parameters, no output schema but return value described), the description is largely complete. It could mention what happens if the entity is not found, but overall it is sufficient for effective use.

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

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

Schema coverage is 100%, but description adds value by providing concrete examples (e.g., 'AAPL', '0000320193', 'Apple') and clarifying that type is limited to 'company' in v1. This goes beyond the schema's generic descriptions.

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

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

Description clearly states the tool resolves an entity to canonical IDs across Pipeworx data sources, specifying the verb 'resolve' and the resource 'entity to canonical IDs'. It distinguishes from sibling tools like 'ask_pipeworx' or 'discover_tools' by focusing on a specific resolution task that replaces multiple lookup calls.

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

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

Description explicitly states that v1 supports only type 'company' and accepts ticker, CIK, or name as input. It also notes that this tool replaces 2–3 lookup calls, providing context on when to use it. Lacks explicit 'when not to use' guidance but is clear enough.

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

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

In the absence of annotations, the description details the return values (verdict types, structured form, actual value with citation, percent delta) and the underlying pipeline. Missing info on potential errors or limitations (e.g., only US public companies), but otherwise transparent.

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

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

Three sentences, no fluff. The first sentence states the core purpose, the second specifies scope and mechanism, the third lists outputs and benefits. Excellent front-loading.

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 no output schema and minimal parameters, the description covers what the tool does, its inputs, and outputs. It could be improved by noting that the tool only works for specific claim types and companies, but for a v1 it's adequately 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 a single parameter 'claim'. The description adds concrete examples ('Apple's FY2024 revenue...') and clarifies the natural-language format, going beyond the schema's bare 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 precisely states the tool fact-checks natural-language claims against authoritative sources, specifies supported domains (company-financial claims for US public companies), and lists the source (SEC EDGAR + XBRL). This clearly 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?

The description explains that the tool replaces 4-6 sequential agent calls, implying efficiency over alternatives. However, it does not explicitly state when not to use it or list alternative tools for out-of-scope claims.

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