Onedrive

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

Annotations already indicate readonly, idempotent, non-destructive. The description adds context about model options, free vs. BYO key, and cost implications. This goes beyond annotations without contradiction.

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

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

The description is four sentences with no redundancy. First sentence states core purpose and output. Second explains model options. Third describes return format. Fourth lists use cases. Ideal 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?

For a tool with 4 parameters and no output schema, the description covers inputs, behavior, and output structure well. Returns are described per-model. Missing details like rate limits or pagination are not critical given annotations. Adequate for effective 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%, so baseline is 3. The description adds value with examples for 'entity', clarifies when '_apiKey' is needed, and explains the 'context' parameter for disambiguation. This enhances understanding beyond schema alone.

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

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

The description clearly states the tool probes LLMs for knowledge about an entity and returns a visibility score (0-100). It specifies the default model and optional Anthropic integration, and gives concrete examples. This is specific and distinguishes from broader search tools, though not explicitly differentiating from sibling 'scan_competitor_ai_presence'.

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

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

The description provides clear use cases: 'AI-marketing audits, pre-launch brand checks, competitive monitoring.' It does not explicitly exclude alternatives or say when not to use, but the context is sufficient for appropriate 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 declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive. The description adds useful behavioral context beyond annotations: it routes among 5,439 tools, fills arguments, returns stable citation URIs, works on every tier, and is a single fast call. This clarifies the underlying mechanism and constraints without contradicting annotations.

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

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

The description is long but efficiently packed: it opens with a strong directive, lists coverage areas, includes concrete examples, and closes with escalation paths. Each sentence contributes value, though a few clauses could be tightened. The structure front-loads the most important usage guidance.

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?

Even without an output schema, the description explains the return format: 'structured answer with stable pipeworx:// citation URIs.' It covers the tool's role as a default entry point, gives examples, and outlines when to escalate. The lack of explicit failure modes or output details is minor given the tool's broad router nature.

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

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

Schema description coverage is 100%, with all six parameters being aliases for 'question' and clearly described. The description reinforces natural-language usage with examples but does not add substantive meaning beyond the schema. Baseline of 3 is appropriate since the schema handles parameter semantics well.

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: it routes questions to the appropriate tool among 5,439 tools and returns structured answers with citations. It explicitly says 'PREFER OVER WEB SEARCH' and lists the types of data it covers, making it distinct from siblings like ask_pipeworx_grounded and deep_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 provides explicit when-to-use guidance: 'START HERE for most questions,' 'Use whenever the user asks...' and distinguishes from alternatives: 'for a hallucination-resistant single answer... use ask_pipeworx_grounded; for a broad/multi-part question... use deep_research.' It also mentions when web search is not needed.

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 annotations already declare read-only/idempotent, and the description adds context that this is an experimental edge with candidate routing improvements under test, that results are compared against stable, and that it currently matches ask_pipeworx exactly. This goes beyond the structured annotations.

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

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

The description is three sentences and mostly front-loaded, but includes a somewhat verbose detail about the retired candidate (date) and a redundant 'Falls back to nothing' clarification, so it earns a 4 rather than 5.

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

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

For a single-question router with no output schema and rich annotations, the description provides essential context about beta status, current equivalence to stable, usage guidance, and the fact that it's a full router. Nothing critical 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 all six parameters documented as aliases for question. The description does not add parameter-level detail beyond noting the same arguments as ask_pipeworx, so the baseline 3 applies.

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 identifies the tool as a beta version of ask_pipeworx, a universal router with 5,439 tools, and distinguishes it from the stable ask_pipeworx by its experimental routing changes. It specifies what it does and how it relates to 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?

It explicitly states when to use it: 'Use it exactly like ask_pipeworx when you want the newest routing,' and contrasts with the stable router. It also provides the current state (no active candidate) to guide decision-making.

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?

Goes beyond annotations by detailing the extraction-only guarantee, refusal conditions (not_in_source, no_tool_match, etc.), the extra LLM call cost, and the return shape on both success and failure. No contradiction with readOnlyHint/openWorldHint.

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?

Packed with high-value information: purpose, mechanism, return structure, refusal reasons, use cases, and cost tradeoff. Every sentence earns its place, and it is front-loaded with the core description.

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 fully explains the return values, evidence format, refusal reasons, and error modes. It covers the tool's complexity and tradeoffs, leaving no critical gaps.

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

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

Schema has 100% description coverage, so the baseline is 3. The description adds no parameter-specific details beyond what the schema already says, but the schema fully documents the aliases and natural language requirement.

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 a specific verb+resource: a hallucination-resistant answer mode that extracts answers solely from tool results. It distinguishes itself from the sibling ask_pipeworx by explaining the same routing but with extraction and refusal behavior.

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 (quoted/cited/acted-on answers, high-stakes) and when not to (casual lookups, prefer ask_pipeworx). Names the alternative and even mentions the cost tradeoff, providing clear decision criteria.

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 goes far beyond annotations by detailing resolution handling (low_confidence_match, market_closed_or_inactive), tradeability warnings, cancellation rules, fallback behavior, parent event extraction, and safety features. Annotations already indicate read-only/idempotent, and the description adds extensive context without contradiction.

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

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

The description is very long (multiple paragraphs) and includes extensive detail that could be relegated to reference docs. However, it is well-structured with section headings and front-loaded purpose, making it navigable despite verbosity.

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 absence of an output schema, the description thoroughly explains the return structure (result.market, analysis, evidence, resolver contract, parent event, etc.) and covers edge cases like low confidence, closed markets, and wide spreads. It is remarkably complete for a complex tool.

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

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

All three parameters are described in the schema with 100% coverage. The description adds context like market format flexibility, depth choices, and include_raw behavior, plus examples. This adds moderate value over the schema alone.

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

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

The description clearly states the tool researches a Polymarket bet into a single call, specifying inputs (slug, URL, question text) and outputs (evidence packet + comparison). It distinguishes itself from simpler queries by emphasizing the 'one call' and 'fan-out' behavior, differentiating it from sibling tools like ask_pipeworx.

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

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

The description provides explicit use cases: 'Use for "should I bet on X", "what does the data say about Y", or "is there edge in Z"'. It includes classifiers and fan-out examples to guide selection. However, it does not explicitly state when not to use the tool or directly compare to alternatives, leaving slight ambiguity.

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

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint—all true/false appropriately. The description adds crucial details: handling of off-calendar fiscal years, specific financial fields pulled, and citation URIs returned. 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 single dense paragraph; it is efficient but could benefit from slight structuring (e.g., separate bullet for company vs drug). All sentences earn their place, and the key usage examples are front-loaded.

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

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

Given the tool's complexity (two entity types, multiple data points, cross-entity comparison), the description covers all essential aspects: data sources, sorting behavior, off-calendar handling, and return format. No gaps remain for an agent to understand usage and output.

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 enriches parameter semantics by explaining how 'values' should be formatted per type (tickers/CIKs vs drug names) and that 2–5 items are expected. It clarifies the 'type' parameter's effect on data sources.

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 opens with natural language examples that instantly convey the tool's purpose: side-by-side comparison of 2–5 companies or drugs. It specifies the data sources (SEC EDGAR/XBRL for companies, FAERS for drugs) and metrics, clearly distinguishing it from siblings like entity_profile or sequential lookups.

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 'ALWAYS PREFER over sequential single-pack lookups when comparing entities', giving a clear directive on when to use this tool. It also explains the behavior for each entity type and that results are sorted by primary metric, providing practical guidance for interpreting output.

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 many behavioral traits beyond the annotations: account and pricing requirements, parallel decomposition across 5,439 tools, return packet structure (findings with citations, gaps[], contradictions[]), excerpting behavior, and expected latency. It does not contradict the readOnly/destructive hints, and even adds nuance to openWorldHint by clarifying it is NOT open-web search.

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 long and dense, but every sentence provides substantive information. It is not as concise as the ideal two-sentence example, and the account requirement opens the description instead of leading with the core purpose. However, given the tool's complexity, the structure is acceptable and there is no fluff or repetition beyond a minor mention of 'not open-web search' twice.

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 having no output schema, the description is exceptionally complete: it explains what the tool does, how it works internally, what returns are like, when to use alternatives, account prerequisites, limitations for non-catalog topics, and timing expectations. It covers all necessary context for an agent to invoke 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%, with detailed descriptions for both 'question' and 'depth' parameters, including enum semantics and paid tier. The description adds little beyond the schema—it restates depth behavior but does not introduce new parameter-level meaning. Since the schema does the heavy lifting, a baseline of 3 is appropriate.

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

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

The description clearly states the tool performs 'grounded multi-source research across Pipeworx's 1412 STRUCTURED data sources' in ONE call, which is a specific verb+resource combination. It explicitly distinguishes itself from open-web search and siblings like ask_pipeworx, making its purpose unmistakable.

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 guidance: 'For a single lookup use ask_pipeworx' and 'For BREAKING or colloquial CURRENT-NEWS ... prefer ask_pipeworx'. It also clarifies account requirements and that ask_pipeworx works without sign-in, giving clear when-to-use and when-not-to-use 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 declare readOnlyHint, idempotentHint, and no destructiveness. The description adds that results are 'ready to call directly, no second schema lookup needed,' which provides useful behavioral insight beyond the annotations. However, it does not elaborate on rate limits or 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?

The description is dense but well-structured. It starts with a concise purpose statement, then details return value, usage context, and parameter aliases. A minor improvement could be splitting into two shorter paragraphs, but it's informative 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 complexity of tool discovery, the description covers all necessary aspects: what it does, what it returns, when to use it, and parameter details. The absence of an output schema is adequately compensated by explaining the result format (names, descriptions, full schemas).

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 baseline is 3. The description adds value by listing aliases for the query parameter (task, q, description, search) and explaining the limit parameter. This clarifies the flexible input format.

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 specifies the return value (top-N tools with full schemas) and distinguishes itself from sibling tools by being the discovery gateway to call first.

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

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

Explicit guidance to 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This tells the agent when to use the tool and provides context for prioritization.

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, idempotentHint, etc. The description adds operational context beyond annotations: it fans out across multiple APIs, performs one parallel call, and notes soft-failure for patents. It does not contradict 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 dense but efficient, starting with usage patterns and then listing return fields. Every sentence adds value. However, the list of return fields could be more structured.

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

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

Given the tool's complexity (multiple sources, no output schema), the description sets good expectations by listing key return fields and limitations. It lacks exact nested structure details but is reasonably complete 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?

Schema coverage is 100% with detailed descriptions. The description echoes the schema's parameter guidance (e.g., 'Names not supported') but does not add significant semantic meaning beyond what the schema already provides. Baseline 3 is appropriate.

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

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

The description starts with many example queries and clearly states it provides a 'full cross-source profile of a US public company in ONE parallel call.' It distinguishes from sibling tools by explicitly preferring over chaining single-pack lookups and lists the specific data sources and return fields.

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 it (holistic view of a US public company), what to avoid (names not supported, use resolve_entity first), and notes a specific limitation (patent data soft-fails after May 2025). It also contrasts with chaining single-pack lookups.

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

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

Annotations already provide destructiveHint=true and idempotentHint=true. The description adds context about clearing sensitive data but does not elaborate on side effects beyond deletion.

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

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

Two sentences with no wasted words. Front-loaded with action and resource.

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

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

Single parameter fully documented in schema. Description covers purpose, usage, and relationships. No output schema needed for this straightforward tool.

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

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

The schema already describes the 'key' parameter as 'Memory key to delete' (100% coverage). The description adds no additional semantic 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 verb 'Delete' and resource 'previously stored memory by key'. It distinguishes from sibling tools by mentioning pairing with 'remember' and 'recall'.

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 provides when to use: 'when context is stale, the task is done, or you want to clear sensitive data'. Also suggests pairing with other tools for 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?

Annotations already indicate readOnlyHint, idempotentHint, and nondestructive behavior. The description adds process details: 'Fetches the page, extracts title/description/key links, and emits the standard llms.txt markdown format.' This goes beyond annotations by explaining what happens during invocation.

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 concise sentences followed by a bullet list of use cases. It is front-loaded with the main purpose and contains no unnecessary words or redundancy.

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

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

Given the tool has only 2 parameters, full schema coverage, and rich annotations, the description covers the essential aspects: what it does, how it works, and when to use it. The output format is described as 'a single text blob', which is adequate for a tool without an output schema.

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

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

Schema coverage is 100% with both parameters fully described. The description mentions 'for any URL' and implies default/max for max_links through context, but does not add significant new semantics beyond the schema. Baseline of 3 is appropriate.

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

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

The description explicitly states the tool generates a 'production-ready llms.txt file for any URL', clearly identifying the verb (generate), resource (llms.txt), and scope (any URL). This distinguishes it from siblings like 'ai_visibility_check' or 'scan_competitor_ai_presence', which have 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?

The description provides explicit use cases: 'getting a client's site indexed by AI, drafting llms.txt for your own project, or auditing how an AI crawler would see a competitor.' While it doesn't explicitly state when not to use it or name alternatives, the context is clear and sufficient for an agent to decide.

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

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

Annotations already indicate safe read operation; description adds value by specifying return fields and default behavior (active only). 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?

Two sentences, front-loaded with action, no wasted words. 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?

Despite no output schema, description fully explains return fields and typical usage, making it complete for the tool's complexity.

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

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

Schema covers 100% of parameters with descriptions, so description doesn't need to add more. 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 'List the caller's active subscriptions' and specifies the exact fields returned, distinguishing it from sibling tools like subscribe and unsubscribe.

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 guides when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Could more directly state when not to use, but the context is clear.

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

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds value by specifying the returned metadata fields (id, name, size, etc.), providing context beyond annotations without contradiction.

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

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

Two concise sentences that front-load the action and key details, with no extraneous 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?

For a simple read-only tool with one parameter and good annotations, the description covers purpose, usage context, and return fields completely.

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 clear description for the 'id' parameter. The tool description restates that it identifies the item by ID, but adds no additional 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 retrieves full metadata for a single OneDrive file or folder by ID, listing specific fields returned. It distinguishes from siblings by advising use after listing or searching to inspect one document.

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 guides when to use this tool ('Use after listing or searching to inspect one document'), but does not provide a when-not-to-use or mention alternatives like onedrive_get_file_content for content retrieval.

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 readOnly, idempotent, non-destructive. The description adds critical behavioral traits: content is capped at ~100,000 characters and flagged when truncated; binary formats return unreadable bytes. This provides complete 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?

Two sentences, front-loaded with the core action, no redundancy. Every word adds value: usage context, format warnings, and size limit. Highly efficient.

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

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

Given a single required parameter and no output schema, the description fully covers input (id), output behavior (text content, truncation, binary issues), and limitations. No gaps.

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

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

Schema has 100% coverage with one parameter 'id' described as 'The OneDrive drive-item id of the file to read.' The description repeats 'by its item id' but adds no new semantics. Baseline 3 applies as schema already fully 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 clearly states the tool downloads and returns text content of a OneDrive file by item id, specifying compatible formats (plain-text, Markdown, CSV) and distinguishing from binary files. This differentiates it from siblings like onedrive_get_file (metadata) and onedrive_list_files (list).

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 indicates best use cases (plain-text files) and warns against binary formats. It also notes content cap and truncation flag, enabling informed selection. While it doesn't name sibling alternatives, the guidance is sufficient for correct usage.

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

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

Annotations already indicate read-only, idempotent, non-destructive behavior. Description adds the return fields and confirms it's for profile info, which is consistent and adds value without contradiction.

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

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

Two concise sentences covering purpose and usage with zero wasted words. Front-loaded with the key action and resource.

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

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

Despite lacking an output schema, the description enumerates the return fields, making the tool's behavior fully understandable. No gaps for a simple profile retrieval tool.

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

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

No parameters, so no need for additional explanation. Baseline score of 4 applies as the description doesn't need to compensate for missing parameter info.

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 verb 'Get' and the resource 'user's OneDrive drive profile', listing specific return fields (drive type, storage quota used/total, owner display name). Distinguishes from siblings like onedrive_get_file which focus on files.

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: 'report storage usage or confirm the connected account'. While it doesn't mention when not to use, the context is clear given the narrowly scoped purpose.

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, destructiveHint=false, openWorldHint=true, and idempotentHint=true. The description adds that the tool returns a list with specific fields (id, name, size, isFolder, last-modified time, web URL), which is useful but does not provide further behavioral context like authentication or rate limits. The description does not contradict 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 extremely concise: two sentences covering purpose, usage, return fields, and use case. No redundant or filler words. Every sentence contributes actionable 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 has 2 parameters, no output schema, and annotations providing safety context, the description covers the core functionality, usage instructions, and return fields. It lacks mention of the 'top' parameter for pagination, but the schema covers that. Overall, it is mostly complete for a browsing tool with good schema and annotations.

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 both parameters described. The description adds extra value by stating 'Pass a folder path relative to the drive root' and providing concrete examples ('Documents', 'Documents/Reports'), which aids understanding beyond the schema's formal descriptions. This clarifies the relative path semantics.

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 lists files and folders in a OneDrive folder, specifying the verb and resource. It provides example paths and distinguishes from siblings like onedrive_get_file (which retrieves a specific file) and onedrive_search_files (which searches across folders). The purpose is specific and unambiguous.

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

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

The description explains how to use it: pass a folder path relative to the root, or omit for root. It states 'Use to browse a user's OneDrive documents,' giving a clear usage context. However, it does not explicitly mention when not to use it or contrast with alternative sibling tools, though sibling names are available in the 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?

Annotations already declare read-only, non-destructive, idempotent, and open-world behavior. The description adds value by specifying exactly what is returned (name, web URL, who shared it), which is not in annotations. 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 consists of two efficient sentences: the first states purpose and output, the second gives a use case. No fluff, no repetition, and the key information is front-loaded. Every sentence carries weight.

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 optional parameter, no output schema) and strong annotations, the description covers purpose, output, and usage. It could mention that results are limited by the 'top' parameter, but that is already in the schema. Overall, it provides sufficient context 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?

The only parameter 'top' is fully described in the input schema (max items, default 25). The description does not add any additional meaning or constraint beyond what the schema provides. With 100% schema coverage, scoring 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 lists files and folders shared with the user (Shared with me), identifies returned fields (name, web URL, sharer), and gives a specific use case (find documents shared by colleagues). This distinguishes it from sibling tools like onedrive_list_files (user's own files) and onedrive_search_files (search).

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 a clear use context ('find documents shared by colleagues'), but does not explicitly state when not to use this tool or name alternative tools. However, the intended usage is clear enough that an agent can infer appropriate vs inappropriate scenarios given the sibling tool names.

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

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

Annotations already declare read-only, idempotent, non-destructive. The description adds search scope and return fields, which is useful context without contradicting annotations.

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

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

Two concise sentences: first explains operation and output, second provides usage advice. 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?

For a simple search tool with well-annotated safety and complete schema, the description provides sufficient behavioral and usage context.

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

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

Schema covers both parameters with descriptions. The main description rephrases the query parameter's purpose without adding new meaning.

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

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

The description clearly states the tool searches OneDrive files and folders by keyword across names and content, lists return fields, and is distinct from sibling tools like onedrive_get_file or onedrive_list_files.

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

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

The description implies use for keyword-based document finding but does not explicitly state when not to use or compare 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 provide no safety hints, so the description explains key behaviors: rate-limited to 5 per day, free and doesn't count against quota, team reads daily, and signal affects roadmap. It also discloses that feedback should describe issues in terms of Pipeworx tools/packs, not end-user prompts. This goes beyond annotations.

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

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

The description is concise yet dense with actionable info. It starts with the core purpose, then usage triggers, exclusions, practical tips, and rate limits in a logical flow. Every sentence adds value with no filler.

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

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

For a feedback submission tool with no output schema, the description fully covers: what it does, when to use, scope restrictions, content expectations, rate limits, and downstream impact. It leaves no critical gaps.

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

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

The schema already covers parameters 100%, but the description enriches semantics by explaining each feedback type (bug/feature/data_gap/praise) and giving guidance on what to include in the message, such as not pasting end-user prompts. This adds practical meaning over schema descriptions.

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

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

The description clearly states the tool's purpose: 'Tell the Pipeworx team something is broken, missing, or needs to exist.' It enumerates specific scenarios (bug, feature/data_gap, praise) and distinguishes it from sibling research/query tools by emphasizing feedback submission, not data retrieval.

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

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

Explicitly provides when-to-use triggers ('Use when a tool returns wrong/stale data...') and a clear when-not: tools from other MCP servers should not be reported here. It also advises how to identify Pipeworx tools, offering both inclusion and exclusion 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 declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description goes beyond by revealing caching behavior (5min-1h), data source (CF analytics-engine), and privacy (no PII). This fully discloses what the tool does behind the scenes.

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 one paragraph but uses clear bullet points for use cases. It is concise yet covers purpose, usage, data source, and caching. No superfluous sentences; every sentence adds value.

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

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

Despite lacking an output schema, the description fully explains what is returned (top tools, top packs, total call volume), how it is computed, and its freshness. Given the tool's simplicity, this is sufficiently complete for an agent to understand the tool's functionality.

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

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

The schema already covers the 'window' parameter with enum descriptions. The description adds extra semantic guidance: 'Shorter windows surface what's hot right now; longer windows show steady-state demand.' This adds value beyond the schema, justifying a 4.

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

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

The description clearly states the tool returns trending data (top tools, top packs, call volume) over specified windows. It distinguishes itself from siblings by focusing on aggregated usage statistics rather than direct queries or entity lookups.

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 enumerates three explicit use cases (discovering hot data sources, confirming canonical tools, aligning use cases with agent demand), providing strong context for when to use the tool. It does not explicitly list when not to use it, but the positive use cases are sufficient.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds significant behavioral context: details on partition checks, semantic anchor for similarity, placeholder filtering, and the fill check that determines realizable 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 relatively long but front-loads the purpose in the first sentence. Every sentence adds value, covering modes, semantic anchor, partition filter, response structure, and fill check. A bit dense but appropriate for the tool's complexity; could be slightly more structured with bullet points.

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

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

Despite no output schema, the description thoroughly explains the response format (opportunities[] with gap_pp, suggested_trade, etc.) and even covers edge cases like fill check and placeholder filtering. It also references related tool polymarket_fill_risk for custom sizing, ensuring the agent knows where to go for more.

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% (both event and topic have descriptions), and the tool description greatly enriches understanding. It explains the purpose of each parameter in detail, provides example values ('fed-decision-may-2026'), and describes the behavioral difference between event and topic modes.

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 starts with a clear verb-resource statement: 'Find arbitrage opportunities on Polymarket via monotonicity violations + partition-sum checks.' It distinguishes itself from sibling tools (e.g., polymarket_edges) by focusing on arbitrage detection, making its unique purpose immediately evident.

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 each argument: no args for trending, event for specific market, topic for cross-event. It also gives recommendations (e.g., 'event (recommended for a specific market)'). While it mentions a fill check filter to avoid non-realizable trades, it does not contrast with alternatives like polymarket_fill_risk in terms of when to use this tool vs. others.

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 readOnly, openWorld, idempotent, non-destructive. The description adds extensive detail: explains three model families, caching at KV level keyed on all knobs, and computation details like slippage subtraction and Kelly capping. 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 very long (over 400 words) and dense, but it is front-loaded with the main purpose. Some sections could be more concise or structured with bullet points, but it remains informative. Given the complexity, it is acceptable but not optimally 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 is provided, but the description details the response structure including by_segment segments, fed_candidates, and _diagnostics. It covers all aspects of the tool's behavior, parameters, and response format, making it complete for an agent to use effectively.

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

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

Schema coverage is 100%, and the description adds significant meaning beyond the schema. For example, it explains why slippage_pp default is 0.3, what min_partition_leg_kelly does for partitions, and provides practical guidance like 'Bump for very thin partitions; drop to 0 if you have a smarter fill model.'

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 scans Polymarket markets for opportunities where Pipeworx data disagrees with market price, and the first sentence 'Scan top Polymarket markets and return opportunities where Pipeworx data disagrees with market price' provides a specific verb-resource pair. It distinguishes itself from sibling tools like polymarket_arbitrage and polymarket_edge_tracker by focusing on disagreement scanning rather than pure arbitrage or tracking.

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 'Built for "what should I bet on today" — agents discover opportunities without paging hundreds of markets,' providing clear usage context. It also notes Fed bets are excluded from ranking, but does not explicitly state when NOT to use it. However, the differentiation from siblings implies usage boundaries.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds substantial behavioral details: response structure (tracked, expired, snapshot_dates), limits (60-day TTL), and that decay is based on daily closes. 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 appropriately sized given the complexity. It fronts the core purpose, then describes args, response, and limits. Every sentence adds value, though the response section is somewhat lengthy but necessary due to lack of output schema.

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 fully explains the return fields (tracked, expired, snapshot_dates) and their subfields (trend, decay_pp_per_day, lifespan_days). It also notes limitations (60-day TTL, daily closes). Minor improvement would be to explicitly state the response is JSON.

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 coverage is 100% with descriptions for both parameters. The description repeats and slightly expands on the schema (e.g., 'clamp 2-30' is already in schema). It adds no critical meaning beyond the schema, so 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 provides 'edge persistence and decay telemetry' and answers 'how long has this edge existed and is it shrinking?' It distinguishes itself from sibling polymarket_edges by focusing on historical tracking rather than current edges.

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 context on when to use the tool ('a fresh wide edge and a 3-week-old wide edge are different trades'), implying it's for evaluating edge age and decay. It does not explicitly list exclusions but the sibling tools provide 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 readOnly, idempotent, non-destructive. Description adds behavioral specifics: walks the order book ladder, returns verdicts (clean/degraded/cannot_fill), and describes basket fill risks. 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 thorough but somewhat lengthy. However, it is well-structured with uppercase mode labels and clear separation of single-market vs basket details. The first sentence captures purpose, and information is front-loaded. Could be trimmed slightly, but remains 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?

Compensates for missing output schema by detailing return fields for both modes (e.g., top_of_book, vwap_fill_price, capture_ratio, per-leg detail). Covers edge cases like thin_legs and forced_directional_risk. Complete enough for an agent to use correctly.

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

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

Schema coverage is 100%, but description adds significant modal context: explains how 'side' and 'size_usd' differ between single-market and basket modes. This goes beyond the schema descriptions, providing clear usage semantics.

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's purpose as a 'realizable-vs-theoretical edge check against live CLOB order-book depth' and distinguishes between single-market and basket modes. It explicitly differentiates from siblings like polymarket_arbitrage by advising to use this tool before acting on arb signals.

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 when-to-use guidance: 'USE THIS before acting on any polymarket_arbitrage SELL/BUY-EVERY-LEG signal or any polymarket_edges trade above ~$500.' Also explains consequences of not using it (unhedged directional positions), effectively serving as a best-practice advisory.

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 readOnlyHint and other annotations, description details compatibility warnings, temporal alignment, skipped counters, and the probabilistic nature of spreads, providing full behavioral context.

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

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

Well-organized into sections with clear labels, but slightly verbose; however, every part adds value.

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

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

Comprehensive coverage of modes, response structure, safety fields, and edge cases; no output schema needed given detailed description.

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 provides descriptions and examples; description adds context on how parameters interact (e.g., override behavior) and lists topic values explicitly.

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

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

Description clearly states it is a cross-venue spread tool, specifies two modes (topic shortcuts and explicit tickers), and distinguishes it from siblings by focusing on Kalshi-Polymarket comparisons.

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

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

Explicitly explains when to use (for spread analysis), when not (when compatibility warnings fire), and provides guidance on mode selection and warnings about tradeability.

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, idempotentHint, destructiveHint. Description adds scoping detail and dual behavior (retrieve vs. list). 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?

Three sentences, front-loaded with action, each sentence adds necessary information: action, use case, scoping, and pairing. No wasted words.

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

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

Given the tool's simplicity and rich annotations, the description is complete. It could optionally mention the return format, but not essential.

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 explaining the optional key parameter and its effect (omit to list all keys). This clarifies behavior beyond the schema's definition.

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 value by key or lists all saved keys, distinguishing it from siblings 'remember' and 'forget'. It uses specific verbs and provides examples like 'user's target ticker'.

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 the agent stored earlier... without re-deriving it from scratch'. Implicitly excludes alternatives by mentioning 'pair with remember to save, forget to delete'.

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?

Description discloses the mark_read side-effect, which is critical for agent understanding. But this directly contradicts the annotations' readOnlyHint: true, creating a factual conflict. The description adds context beyond annotations but at the cost of inconsistency.

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 covering purpose, return fields, filters, side effects, and alternatives. Front-loaded with critical info, 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?

Describes return values, filtering, side effects, and alternative endpoint. No output schema, but covers key agent needs. Could mention pagination or rate limits, but overall adequate for a read-like tool with 5 parameters.

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

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

Schema coverage is 100%, so baseline is 3. The description adds extra value by explaining mark_read's effect on subsequent calls and providing examples for type and since, raising the score to 4.

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

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

The description clearly states it pulls fired events from the subscription feed, listing specific returned fields (source, citation_uri, raw payload). This verb+resource distinction sets it apart from siblings like list_subscriptions.

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 filtering options (type, since), mark_read behavior, and notes that polling works and an alternative endpoint exists. However, it does not explicitly state when to avoid this tool or compare to siblings like list_subscriptions.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint=false. The description adds valuable context: data sources (SEC EDGAR, GDELT/GNews fallback, USPTO), window format, and return structure (changes[] grouped by source, total_changes, 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 fairly long but every sentence adds value. It is front-loaded with usage examples. Minor room for trimming but not wasteful.

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 required parameters and no output schema, the description fully covers input semantics, output format, alternative tool, and behavioral traits. Complete 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%, but the description adds significant meaning: explains type is only company, value can be ticker or CIK with example, since accepts ISO date or relative shorthand examples. Exceeds baseline.

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 aggregates changes (SEC filings, news, patents) for a company over a time window. It provides specific examples like "What's new with X" and explicitly distinguishes from sibling tool 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 when-to-use guidance with examples, explains the supported entity type (company only), and contrasts with entity_profile for static profiles. No ambiguity.

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

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

Description adds context beyond annotations: key-value scoping by identifier, session vs persistent memory, 24-hour retention for anonymous. Does not contradict idempotentHint or other 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?

Four well-structured sentences, front-loaded with purpose, no wasted words. Efficiently covers purpose, usage, and behavior.

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 (write tool), description fully covers storage behavior, persistence, and pairing with recall/forget. Complete for all relevant aspects.

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. The description adds practical examples of key values (e.g., 'subject_property'), providing extra semantic guidance 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 saves data for reuse across conversations/sessions with specific examples (ticker, address, preference, research subject). It distinguishes from siblings recall and forget.

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

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

Explicitly says when to use: when discovering something worth carrying forward. Provides context on persistence (authenticated vs anonymous) and mentions paired tools recall and forget.

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, and idempotent behavior. The description adds behavioral details: it cascades through multiple lookup endpoints, returns specific fields with citation URIs, and auto-disambiguates input. No contradiction with annotations.

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

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

The description is somewhat verbose but well-structured: it starts with examples, then states the primary use case, then details supported types. Every sentence adds value, though some could be tightened.

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

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

Without an output schema, the description adequately explains return values (ticker, CIK, company name, citation URI for company; RxCUI, ingredient, brand, citation for drug). It covers auto-disambiguation and internal cascading. For a lookup tool with robust annotations, this is fairly complete.

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

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

Schema coverage is 100%, so baseline is 3. The description adds significant meaning by explaining acceptable input patterns (ticker, CIK, or name for company; brand or generic for drug) and what the output includes (ticker, CIK, company name, RxCUI, etc.), which goes beyond the 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 uses specific verbs ('resolve', 'look up') and clearly identifies the resource ('name to canonical ID'). It provides example queries and explicitly distinguishes itself from siblings with 'Use FIRST whenever you have a name but need an ID.'

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 using this tool first when you have a name but need an ID, and provides concrete use cases (ticker, CIK, RxCUI). It also explains that it replaces 2-3 manual lookups. It does not state exclusions, but the context is clear.

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

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

Annotations already indicate read-only, open-world, idempotent, non-destructive. The description adds behavior: probes each entity with ai_visibility_check, ranks by score, and treats first entity as subject. 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?

Three sentences with front-loaded purpose, method, and use case. 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 no output schema, the description explains the return value (ranked list with score, confidence, signal density). It covers all parameters' behavior and expected output format completely.

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 significant meaning: entities first is subject, models default to workers-ai, context disambiguates. This goes beyond schema and aids 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 clearly states the tool compares AI visibility across multiple entities, using ai_visibility_check, and ranks results. It distinguishes from sibling ai_visibility_check (single entity) and provides a concrete use case example.

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 competitive AI-marketing audits and gives an example question. However, it does not explicitly state when not to use or list alternatives like compare_entities or single-entity checks.

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 safe, read-only, idempotent behavior. Description adds critical details: composite fan-out, partial failures, bundlephobia timing (5-30s), graceful degradation with sources_failed field. This goes well beyond the 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?

Well-structured, front-loading composite nature then usage then returns. Every sentence is valuable. Slightly lengthy but justified by the tool's complexity.

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

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

Despite no output schema, description details return fields (summary, advisories, links, alternative versions). Covers parameters, behavior, failure modes, ecosystem constraints. Complete for a complex composite tool.

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

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

Schema coverage is 100%, so baseline is 3. Description adds minor context: scoped packages are accepted, version defaults to latest. Does not add significant meaning beyond schema, but adequately compensates.

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's a composite check for npm packages, combining deps.dev and bundlephobia. It answers 'is X safe / popular / small' or 'what does adding lodash cost me'. This distinctly separates it from other tools like resolve_entity or validate_claim.

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

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

Explicitly says 'Use whenever an agent asks...' and limits to NPM ecosystem, mentioning alternatives for other ecosystems. Provides concrete 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?

Beyond annotations (readOnlyHint, etc.), description adds implementation details: BGE-base-en embeddings, cosine similarity, 500-char overlapping windows, 200K char cap with truncation flag. 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?

Four dense sentences, each with purpose: defining action, use case, pairing info, and technical details. No redundancy. Front-loaded with core purpose.

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

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

Despite no output schema, description explains return format (passages with offsets and scores), covers truncation behavior and cap. All parameters are fully described. Complete for a search tool.

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

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

Schema description coverage is 100%. Description enhances parameter understanding: clarifies text max chars, gives query examples, explains limit default and range. Adds moderate 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?

Description clearly states it performs semantic search inside a fetched record, with specific examples like SEC filings and articles. It distinguishes from siblings by mentioning pairing with ask_pipeworx_grounded.

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 the record is too large for the prompt. Provides guidance on saving context and verifying quotes via offsets. Does not explicitly say when not to use, but context is clear.

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

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

Annotations indicate readOnlyHint=false, destructiveHint=false, idempotentHint=true, openWorldHint=true. Description adds behavioral details: requires authenticated account, webhook auto-disabled after 10 failures, return of subscription id. 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?

Description is dense but well-organized: front-loaded with purpose and return, then requirements, types, delivery. Every sentence adds value. Could benefit from bullet points for readability, but current structure is efficient.

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 purpose, requirements, types, delivery options, and limitations. Missing details on return format beyond 'id', and no error cases. For a subscription creation tool with no output schema, it is fairly complete but could improve.

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 significant meaning: explains each subscription type, provides concrete parameter examples (e.g., ticker, items), and elaborates on delivery options (sms verification, webhook signing secret, auto-disable). Greatly enhances agent 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 'Create' and resource 'proactive monitoring subscription', with explicit return value 'Returns the new subscription id'. It distinguishes from siblings like 'list_subscriptions' and 'unsubscribe' by focusing on creation.

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

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

Provides clear context: requires Pipeworx OAuth account, lists supported types with examples, and delivery channel limitations (phone verification, 10/day cap). Does not explicitly state when not to use or name alternatives, but context is sufficient.

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

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, destructiveHint. Description adds context about returning example questions drawn from a live catalog and calling behavior (no args vs topic). 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 somewhat long but well-structured: starts with example queries, explains return type, then usage instructions. Every sentence is informative, but minor redundancy could be trimmed.

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 role (onboarding with example questions) and minimal schema, the description adequately covers purpose, input, and output nature (category-bucketed examples with tool shapes). No output schema, but description compensates.

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% for the optional topic parameter. Description adds meaning by listing example values ('finance', 'pharma', etc.) and explaining the effect ('focus on that area') and the default behavior ('omit for cross-category spread').

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

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

The description explicitly states it is the onboarding entry point that returns category-bucketed example questions with tool+argument shapes. It uses specific verbs ('Returns category-bucketed example questions') and clearly distinguishes from sibling meta-tools like ask_pipeworx.

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

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

The description advises to 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also explains when to pass a topic (to focus) and when to omit (for full spread). No explicit when-not-to-use but context is clear.

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

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

Beyond annotations (readOnlyHint: false, destructiveHint: false, idempotentHint: true), the description adds key behaviors: the row is deactivated not deleted, and historical events remain available via recent_alerts. This provides valuable behavioral context.

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

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

Two sentences with zero waste. The critical information is front-loaded (cancel, ownership, deactivation) and efficiently communicated.

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 one-parameter tool with no output schema, the description covers purpose, ownership, side effects, and connects to related tools (subscribe, recent_alerts). It is fully sufficient for an agent to use correctly.

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

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

With 100% schema description coverage, the schema already documents the id parameter as a UUID from subscribe. The description does not add extra parameter information, so it meets the 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?

The description clearly states 'Cancel a subscription by id' with a specific verb and resource. It also explains ownership enforcement and the deactivation vs deletion distinction, distinguishing it from siblings like subscribe and recent_alerts.

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 mentions that ownership is enforced ('you can only cancel your own subscriptions'), guiding the agent on when it's appropriate. It does not explicitly state when not to use or mention alternatives, but the 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?

The description goes beyond annotations by detailing the internal routing (SEC EDGAR for financial, grounded pipeline for other), the exact percent-delta math, the returned verdict values, and the efficiency gains (replacing 4-6 calls). 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 slightly long but well-structured, front-loading examples and purpose. Every sentence adds value, and the structure guides the agent from purpose to details to output.

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

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

No output schema is provided, but the description fully explains the return values (verdict, actual value with citation, reasoning). It covers all necessary aspects: when to use, how it works, what it returns, and internal routing. No gaps identified.

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

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

Schema coverage is 100%, so baseline is 3. The description adds semantic value for 'tolerance_pct' by explaining how it overrides implied tolerance and provides a use case (hallucination detection). This justifies a score of 4.

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

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

The description clearly defines the tool's purpose as verifying factual claims, provides natural-language examples ('Is it true that…', 'fact check'), and explicitly states when to use it. It distinguishes itself from siblings by focusing on claim verification with a specialized pipeline.

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 'Use whenever the agent needs to check whether something a user said is factually correct.' It also describes internal routing (financial vs. other), providing clear context. However, it does not explicitly state when not to use the tool or mention alternatives, leaving some ambiguity.

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