ClearMarket

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

No annotations provided, but the description fully discloses behavior: includes cross-venue links, caveat about shared question_id not implying arbitrage, and detail level behavior ('concise' vs 'full').

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 well-organized with colons and lists. Every sentence adds value, though slightly verbose; could be tightened slightly.

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

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

For a tool with no output schema and no annotations, the description covers the complex return structure, caveats, and usage guidance thoroughly, leaving no major gaps.

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

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

Schema coverage is 100%, but the description adds significant meaning beyond schema: clarifies slug can be CM event id, explains detail parameter use cases and default, and describes what each detail level includes.

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 specific verb ('Fetch') and resource ('ClearMarket record for one event by slug'), and immediately lists the returned data, distinguishing it from siblings like get_market and list_events.

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 (need authoritative graded view of a specific event) and when not ('If you only have a topic, call list_events first'), and guides on detail parameter choices.

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

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

With no annotations, the description fully discloses behavior: it accepts multiple ID formats (best-effort for URLs), returns a canonical ID, and provides detailed output including trust judgment via rcg. No contradictions.

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

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

The description is front-loaded with the core purpose, then efficiently details input and output. Every sentence serves a purpose; 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?

Despite no output schema, the description enumerates all return fields (question, price, rcg, provenance, shape, etc.) and explains the canonical ID reuse, making it complete for the tool's use.

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

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

Schema coverage is 100%, but the description adds value by noting best-effort resolution and instructing to pass conditionId for Polymarket, going beyond the schema's generic description.

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

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

The description clearly states the verb ('Fetch') and resource ('one market'), with a specific judgment function ('judge whether its price can be trusted'), which distinguishes it from sibling tools like 'get_event' or 'get_signal'.

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

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

The description explicitly says 'Use before trusting or acting on a price,' providing a clear use case. It does not contrast with alternatives, but the context makes the tool's role evident.

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

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

With no annotations, the description carries the full burden. It accurately describes the tool as read-only ('Fetch') and details the comprehensive return contents (headline, bullets, claims, events, markets, sources). No side effects or permissions are needed, so the description is sufficient.

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

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

Two sentences, zero fluff. The first sentence immediately conveys what the tool does and returns, while the second provides usage guidance. Every word 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, the description thoroughly explains the return structure. It also references sibling tools (list_signals) to anchor usage. This is complete for a simple fetch 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 only parameter 'slug' has full schema coverage with an example. The description adds value by explaining its origin ('from list_signals') and context ('by slug'), making it clear how to obtain valid input.

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 'Fetch' and the resource 'one full CM Signal wire by slug', and lists the included elements. It effectively distinguishes from siblings like list_signals (which provides slugs) and get_event/get_market (different resources).

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 'Use when you have a wire slug (from list_signals) and need the complete bulletin with its proof chain', providing clear context for when to invoke. While it doesn't enumerate alternatives, the guidance is concise and actionable.

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?

Without annotations, the description carries full burden. It discloses search behavior (token-AND across question+tags), return format (compact graded summaries with listed fields), and the effect of category/platform filters. No security or rate-limit info, but for a read search tool this is adequate.

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

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

The description is well-structured and concise: purpose, filter behavior, return fields, workflow advice, category list. Every clause adds value with no redundancy.

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

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

Given 6 parameters, no output schema, and no annotations, the description covers all filters, explains return structure, and provides usage context. It is complete enough for an agent to effectively use this tool.

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

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

Only 33% of parameters have descriptions in the schema, but the description compensates by explaining q's token-AND behavior and listing category options (though platform enum is not explicitly listed). This adds significant 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 browses or searches ClearMarket prediction-market events, with specific filters and return fields. It distinguishes itself from get_event by indicating that list_events is the starting point when a slug is unknown.

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 is given: start here when you have a topic but not a slug, then call get_event for full record. Also explains q's token-AND behavior and recommends short keyword queries, helping the agent avoid natural-language pitfalls.

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

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

With no annotations provided, the description carries full burden. It discloses that the tool returns 'compact records, newest first' and lists available filters. It does not mention rate limits or authentication, but for a read-only list tool this is sufficient.

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

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

Three sentences, each adding distinct value: purpose, filtering options, and guidance on sibling tools. No filler; front-loaded with the core function.

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 returns are 'compact records, newest first.' It mentions limit but not pagination details. Sibling tools are referenced for deeper context. Overall adequate for a list 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 40%, but the description adds meaning for all parameters by explaining filtering semantics (e.g., 'every wire touching a specific event' for event_id). It also implies the limit parameter's default behavior. This compensates for schema gaps.

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 browses 'the CM Signal daily wire — short, structured prediction-market bulletins' and specifies filters by event_id, category, venue, and detection type. It distinguishes from get_signal (full bulletin) and list_events (thematic filtering).

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 to use this tool 'to find ClearMarket's editorial read on what is moving' and mentions alternatives: 'call get_signal for the full bulletin' and 'list_events for thematic filtering.' It also notes the default sort order 'newest first.'

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

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It reveals the tool lists scheduled catalysts, provides provenance to sources (BLS, Fed, etc.), and describes the effect on prediction-market prices. It does not mention output limits or pagination, but the behavior is well explained for a list operation.

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

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

The description is two sentences with no extraneous information. It front-loads the core action and value proposition in the first sentence and adds provenance context in the second. Every word earns its place.

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

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

Given the tool's simplicity (one parameter, no output schema, no annotations), the description is nearly complete. It explains what the tool does, the parameter meaning, and what each entry includes (provenance). It could optionally detail the output structure further, but the current description is sufficient for a list 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 defines a single 'days' parameter with type integer, default 30, and range. The description adds meaning by stating 'in the next N days,' clearly linking the parameter to the look-ahead window. This compensates for the 0% schema description coverage and provides essential context beyond the raw 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 lists scheduled catalysts (CPI, jobs, FOMC, GDP, large-cap earnings) in the next N days, with the specific outcome of moving prediction-market prices before resolution. It distinguishes itself from sibling tools like list_events by emphasizing a cross-event calendar view and provenance to authoritative sources.

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 case ('Use to find what scheduled events will reprice the prediction-market universe soon') and implies the tool is for broad calendar discovery vs. single event or market-specific tools. However, it does not explicitly state when not to use it 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.