Prediction Market X Sentiment

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds meaningful context beyond them: that the call costs nothing and that the catalog is exhaustive ('every route, price, network and pay-to address'), which tells the agent to expect a complete inventory rather than a paginated subset.

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 short sentences with no filler; the cost signal is front-loaded and the content scope follows immediately. 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?

With an output schema present, the description need not explain return values, and for a zero-parameter read-only tool the coverage is nearly sufficient. A brief note on what the catalog is used for (e.g. route discovery or address lookup) would close the remaining 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?

The tool takes zero parameters and the schema has no properties, so there is nothing for the description to clarify; the baseline for a no-argument tool applies. The description correctly does not invent parameters or imply any filtering options.

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 names a concrete resource (a catalog of routes, prices, networks and pay-to addresses) and specifies it is machine-readable, so an agent knows exactly what it returns. It does not explicitly contrast itself with the closest sibling, get_pricing, so it stops short of full sibling differentiation.

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 leading word 'Free' implies when to reach for this tool (cost-free discovery versus a paid pricing call), which is useful implied guidance. However, there is no explicit statement of when to use this over get_pricing or what preconditions apply, so the routing logic is left to inference.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered; the description adds non-annotation context: cost ('Free'), data freshness ('latest daily', 'change since the previous day'), and the exact data shape returned. It does not mention rate limits, caching, or what happens for markets without a score, so it stops short of full transparency.

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 'Free.' and the core payload, then a single routing sentence for the alternative. Every clause carries information (cost, freshness, scope, fields, fallback) 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 zero-parameter read tool with an output schema covering return values and annotations covering safety, the description supplies everything else an agent needs: cost, coverage universe, freshness semantics, and the sibling to use for anything outside that universe.

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 tool takes no parameters (0 of 0 required, 100% schema coverage), so there is nothing for the description to clarify and the baseline is 4. The description correctly describes the tool as operating over the whole tracked market set rather than implying optional filtering inputs.

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?

States a specific verb+resource ('Latest daily X sentiment score for each prediction market we track'), names the covered universe (Polymarket), and enumerates exactly what each record contains: change since previous day, catalyst, and Yes price. It also explicitly distinguishes itself from get_market_sentiment, so an agent can disambiguate without opening either schema.

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 an explicit routing rule for the sibling tool: 'For a fresh score on any other question use get_market_sentiment.' The condition (any question outside the tracked markets) and the alternative are both stated, leaving nothing to inference.

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

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so safety is covered. The description usefully adds that the call is free (a cost/rate-limit-adjacent fact not in the annotations) and hints at the returned content, but says nothing about latency, uptime semantics, or whether it is safe to poll.

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?

A single short sentence with the cost qualifier front-loaded; no waste. It is under-specified rather than verbose.

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?

An output schema exists, so return values need not be described, and the description does gesture at what comes back. However, it omits any guidance on when this probe is appropriate relative to the many sibling market-data endpoints.

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 tool takes zero parameters and schema coverage is 100%, so there is nothing for the description to clarify. Baseline of 4 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 names three content areas (status, prices, routes) but never states the verb or that this is a health/liveness probe, leaving the purpose to be inferred from the name and title. It also overlaps conceptually with siblings get_pricing and get_catalog without explaining the difference.

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?

There is no statement of when to call this versus get_pricing, get_catalog, or the other sibling endpoints. The only cue is the 'Free' marker, which hints at cost rather than usage conditions.

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 say readOnlyHint=false, openWorldHint=true, which is consistent with an external live-odds fetch, and the description adds the key trait the annotations don't convey: this call is paid. It still doesn't say whether credits are consumed per call or how failures on an unknown question behave, so it stops short of 5.

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?

One efficient sentence, front-loaded with the paid caveat before the content list. The enumeration partially duplicates what the output schema already carries, but it aids tool selection rather than padding.

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 100% schema coverage on the one parameter and an output schema covering the returned fields, the remaining need is selection and cost context — both provided. Nothing essential for a correct invocation is missing, though the relationship to sibling market tools is left implicit.

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% and the single 'q' parameter is fully documented in the schema with format, limit and an example. The description adds no syntax or format detail beyond 'a market question', 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?

States a specific resource ('full brief for a market question') and enumerates its contents (score, catalyst, volume trend, delta, summary, live Polymarket/Kalshi odds), which distinguishes it from siblings like get_market_sentiment and get_top_markets. It reads as a content listing rather than a crisp verb+resource, but an agent can tell what it returns.

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?

'Paid (see get_pricing)' tells the agent a cost prerequisite exists and where to check it, which is genuinely useful routing. However, there is no explicit when-to-use vs alternatives guidance — nothing says why an agent would pick this over get_market_sentiment or get_top_markets.

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

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

The description adds a genuinely useful behavioral fact beyond the annotations: the call is paid and pricing lives in get_pricing. Annotations cover the rest of the profile (openWorld, non-idempotent, non-destructive), but the description says nothing about latency, rate limits, or how fresh 'fresh' is.

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 tight clauses, cost caveat front-loaded and output fields enumerated without filler. Slightly dense but no sentence is wasted.

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 an output schema present, the description need not explain return values, yet it still names the score, catalyst and volume trend, and flags the paid model. The missing piece is routing guidance relative to the sentiment/brief siblings.

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

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

Schema description coverage is 100%, so the schema already fully documents the single 'q' parameter with format and length. The description's 'any Polymarket or Kalshi market question' adds a mild domain constraint but no syntax or example beyond the schema. 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?

Names a specific verb/resource (fresh X sentiment) plus the concrete output shape (score -100..100, catalyst, volume trend), and scopes it to Polymarket/Kalshi market questions. It does not explicitly distinguish itself from the nearby get_sentiment_shift or get_market_brief siblings, so the agent must infer the split.

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 only usage signal is 'Paid (see get_pricing)', which tells the agent about cost but not when this tool is the right choice versus get_sentiment_shift or get_market_brief. No when-not-to-use or alternative routing is given.

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

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

Annotations already cover readOnlyHint and openWorldHint, so the safety profile is known. The description adds genuinely useful context beyond them — that the call costs nothing and is denominated in USDC — but says nothing about rate limits, freshness, or scope limits of the returned pricing.

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 terse, front-loaded sentences with zero filler; the cost statement comes first and the scope second.

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 an output schema present, return values need not be described, and a zero-parameter tool has no parameter gaps. What is missing is the surrounding workflow context — that the prices describe the sibling paid tools and how they are consumed.

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 tool takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate beyond what the empty schema already conveys.

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?

States a specific resource (current USDC prices) and scopes it to 'the paid tools', which separates it from the market-data siblings like get_market_brief and get_top_markets. It does not explicitly name or contrast a sibling, so it falls short of a 5.

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 only usage cue is the word 'Free', which hints that this is a pre-flight cost lookup, but there is no explicit when-to-use, when-not-to-use, or named alternative. The agent must infer the workflow entirely.

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 cover the safety profile (readOnlyHint=true), so the description's job is to add context, which it does: it discloses that the payload is static/not live and costs nothing. That liveness and cost disclosure is genuinely useful beyond the annotations, though it says nothing about rate limits or whether the sample is representative of every endpoint.

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?

One short sentence with the cost qualifier ('Free') front-loaded and the core meaning — static sample, not live — stated immediately after. No sentence is wasted on a tool this simple.

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?

An output schema exists, so return values need not be described, and with zero parameters and clear annotations the description is nearly sufficient as-is. The only unstated item is any caveat about how representative the sample is, which is minor.

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 tool takes zero parameters, so the baseline is 4; there is nothing for the description to disambiguate. The description correctly implies a no-input call and adds no misleading parameter claims.

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?

States exactly what the tool returns: a static sample of a sentiment response, explicitly flagged as not live data. The parenthetical '(not live data)' implicitly distinguishes it from live siblings such as get_market_sentiment and get_daily_scores, so an agent can route correctly without opening a schema.

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?

'To preview the shape' gives a clear use context — inspecting response structure before wiring up a real call — and 'Free' signals there is no cost to calling it. It stops short of explicitly naming alternatives or stating when not to use it, so it does not reach a 5.

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 declare a non-read-only, non-idempotent, open-world operation, and the description's only added behavioral signal is the cost notice pointing at get_pricing. It does mention the returned timestamps and catalyst, but that overlaps with the existing output schema rather than disclosing 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?

Two short sentences with no padding, and the cost caveat is front-loaded so an agent sees it before planning the call. Slightly telegraphic phrasing ("Change in X sentiment") costs a little clarity.

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 one-parameter tool with a full output schema and annotation coverage, the definition covers purpose, cost, and output shape adequately. The missing piece is routing guidance relative to get_market_sentiment, which is a minor gap given the otherwise simple surface.

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 schema description coverage at 100%, the schema already documents the single q parameter including format and length limits. The description adds only "for a market question," which is weaker than the schema text, 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 gives a specific verb (change in) and resource (X sentiment for a market question) plus scope (since its previous score). It is clearly differentiable from the current-snapshot sibling get_market_sentiment, though it never names that sibling explicitly.

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?

"Paid (see get_pricing)" tells the agent a precondition exists, but there is no explicit when-to-use vs when-not, and the obvious alternative (get_market_sentiment for a current snapshot) is not named. Usage is only implied by "since its previous score".

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 only declare openWorldHint and non-idempotency, which is thin. The description adds genuinely behavioral facts the annotations do not carry: the tool is paywalled, and the pricing sibling must be consulted. It does not state rate limits, latency, or freshness guarantees beyond "right now".

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, no filler. The cost/paywall notice is front-loaded before the content description, which is exactly the order an agent needs to decide whether it can afford to call it.

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?

An output schema exists, so return shape need not be explained, yet the description still previews the key fields. Cost, scope, and count are all covered; only the ranking criterion for "most discussed" and data staleness are left implicit.

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?

Zero parameters, so there is nothing for the description to disambiguate; baseline for a 0-param tool is 4. Schema coverage is 100% and the schema is empty, so no gap exists.

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?

States a precise verb+resource+scope: the three Polymarket/Kalshi markets most discussed on X right now, plus the payload (sentiment score, catalyst, volume trend). An agent can distinguish this from get_market_sentiment or get_market_brief purely from the description.

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 names a prerequisite and routes to a sibling ("Paid (see get_pricing)"), which is real usage guidance. However, it never says when to prefer this over the other market-oriented siblings (get_market_sentiment, get_market_brief, get_daily_scores), so selection still requires inference.

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