Prediction Market Friction Tracker

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

With no annotations, the description carries the full burden. It discloses that the tool is a description of the feed, not the feed itself, and mentions gating by x402. However, it does not state what the tool actually returns when called (e.g., a static description, an error, or a redirect), nor does it describe any side effects. Partial transparency, but significant gaps remain.

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 provides essential context about the feed's append-only nature, payment/gating, and real-time usage. It is not bloated, though the timestamp mention could be clarified. Overall, it is reasonably concise and each 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?

The tool has no parameters, no output schema, and no annotations, so the description must fully compensate. It explains the concept of the change feed and gating, but fails to specify what the tool actually returns (e.g., a textual description, a list, or a stub). The ambiguous timestamp reference further reduces completeness. An agent would be unsure of the tool's exact behavior.

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 zero parameters, and with 0 params the baseline is 4. However, the description mentions 'since a given timestamp', implying a timestamp parameter that does not exist in the schema. This is misleading and undermines the semantics; it suggests a parameter that cannot be provided, which could confuse an agent.

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 'describes the change feed', a specific append-only log of fee/KYC/withdrawal/API changes. It distinguishes itself from sibling tools (get_venue, list_venues, recommend_venue) which deal with venue information, not change feeds. The verb 'describes' is specific and the resource is well-defined.

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

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

The description explains that the feed is a paid, time-sensitive product gated by the x402 protocol (HTTP 402) rather than exposed here, and advises calling directly for real-time data. This provides clear usage context and implies an alternative (direct x402 call), though it does not explicitly name sibling tools or provide exclusions.

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

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

No annotations are provided, so the description carries the burden. It discloses that the tool is 'Free' and returns 'current' data, which adds behavioral context. However, it does not explicitly state that this is a read-only operation, nor does it mention error behavior (e.g., invalid slug) or any rate limits. The description is adequate but leaves some safety profile implicit.

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

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

The description is two sentences, front-loaded with the core action and data scope. It includes examples, a cost note, and a prerequisite hint without any fluff. 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?

For a single-parameter, no-output-schema tool, the description is complete. It tells what data is returned, how to identify the venue, and what to do if the slug is unknown. There is no obvious missing context that would prevent an agent from invoking 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?

The only parameter, slug, has no schema description, so the tool description fully compensates. It explains that slug is a venue identifier like 'kalshi' or 'polymarket', and gives guidance to call list_venues if the slug is unknown. This clarifies parameter purpose and sourcing beyond the bare 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 one venue's current fee/KYC/withdrawal/API-access data by slug. It names specific data categories and the resource type (venue), and the verb 'Get' is precise. This distinguishes it from sibling tools like list_venues and recommend_venue.

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 context on when to use this tool: when you need details for a single venue, and it tells the agent to call list_venues first if the slug is unknown. It also notes the tool is free, which is a usage consideration. However, it does not explicitly name alternative tools for when one should not use this tool, though it implies list_venues for overviews.

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

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

No annotations are provided, so the description carries the burden. It discloses that the tool is free, lists tracked venues, and returns a snapshot of fee/KYC/withdrawal. It does not explicitly state that the operation is read-only or describe side effects, but the verb 'list' implies a safe read. It lacks detail on pagination or return format, but for a simple list tool this is acceptable.

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 and front-loaded with the main purpose. The 'Free' sentence is extra but not harmful. The Args section is well-structured and every part contributes to understanding. It earns a top score for efficiency.

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), the description is quite complete. It explains what is listed and the filter semantics. It does not explicitly state the return format or limits, but for a list tool this is a minor gap. The lack of annotations is compensated by the clear purpose and parameter guidance.

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

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

Schema description coverage is 0%, so the description must compensate. It fully covers the one parameter (confidence) by listing the allowed values ('verified', 'stale', 'unconfirmed') and explaining when to use each, especially the 'unconfirmed' value. This adds significant meaning beyond the bare 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 begins with 'List all tracked prediction market venues', which is a specific verb+resource statement. It clearly distinguishes from siblings like get_venue (single venue) and recommend_venue (recommendations) by the 'all' scope and the focus on fee/KYC/withdrawal snapshots.

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 context for when to use the tool (to list all tracked venues) and gives specific guidance on the confidence filter, especially the 'unconfirmed' value for finding venues needing human review. However, it does not explicitly mention when not to use it or point to alternatives, though siblings are self-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 full burden and excels: it discloses that the tool is 'Free', that it relies 'Only on the structured fields this tracker collects', and that it 'does NOT have liquidity, trading volume, spread, or uptime data' and 'the response says so rather than guessing'. This is transparent about limitations and behavior.

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

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

The description is well-structured: a purpose statement, usage examples, limitations, and parameter details. Every sentence adds value, and it's appropriately sized for the tool's complexity. No fluff or repetition.

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

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

The description covers purpose, usage, data limitations, and query semantics thoroughly. However, it does not describe the return value or output format (no output schema, so description should clarify what the tool returns, e.g., a single venue name vs. ranked list). This is a minor gap.

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

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

Schema coverage is 0% (no descriptions in the input schema), but the description compensates fully by explaining the 'query' parameter: 'free-text description of what matters (cost, KYC/privacy, withdrawal speed, API access, jurisdiction, custody, etc)' and notes it 'Can combine multiple criteria in one query.' This adds substantial meaning beyond the bare schema.

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

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

The description uses a specific verb ('Recommend') and resource ('tracked prediction-market venue') and clearly scopes the output to 'best fits a natural-language need'. It explicitly distinguishes itself from siblings by stating 'For a single already-known venue use get_venue instead; to browse/filter by confidence use list_venues.'

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 provides explicit usage guidance with examples: 'Use this for comparative "which venue is best for X" questions' and lists concrete query types. It also gives exclusions: 'For a single already-known venue use get_venue instead; to browse/filter by confidence use list_venues.'

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