US government data (USGS/NOAA/SEC/FDA) with cryptographic proof; x402 pay-per-call, no API key
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Average 4.1/5 across 3 of 3 tools scored.
Each tool serves a clearly distinct purpose: find_signal checks coverage/freshness, get_official_record retrieves a payment challenge URL, and verify_citation validates record hashes. No overlap or ambiguity.
All tools follow a consistent verb_noun pattern (find_signal, get_official_record, verify_citation) with snake_case, making naming predictable and clear.
3 tools is slightly low but appropriate for the server's focused scope of pre-payment checks and citation verification. Not excessive or trivial, though one might expect an additional tool for data retrieval after payment.
Covers the core workflow: check coverage (find_signal), obtain payment challenge (get_official_record), verify result (verify_citation). Missing a tool to directly retrieve paid data, but that is intentionally external. Minor gaps in CRUD for signals/entities but not essential for stated purpose.
find_signalFind which signals/entities are covered (free)AInspectFREE coverage + freshness manifest: which signal_id lines exist, how many entities each covers, and fresh/recent/stale counts - so you can check "is my entity covered and how fresh" BEFORE paying. Optional filters: industry, signal_id, entity.
| Name | Required | Description | Default |
|---|---|---|---|
| full | No | true = per-entity detail (larger); default compact summary | |
| entity | No | object id, e.g. a USGS site id | |
| industry | No | ||
| signal_id | No | e.g. hydrology.river-level |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must cover behavior. It indicates the tool is free, returns coverage and freshness counts, and offers a compact summary vs. per-entity detail via the 'full' parameter. However, it does not disclose whether the tool is read-only, potential rate limits, or data size implications.
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 with two sentences. The first sentence states the purpose and output upfront. The second lists optional filters. No unnecessary words 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 explains the return value clearly (signal_ids, entity counts, freshness categories) without an output schema. It covers the core functionality and optional filters. However, it omits details like result size limits, pagination, or error conditions. For a free manifest tool, it is adequately complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema describes all four parameters, with three having descriptions (75% coverage). The description adds little beyond restating optional filters. For 'industry', no schema or description detail is provided. Given the high schema coverage, the description offers marginal added value for parameter understanding.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as providing a free coverage and freshness manifest for signals/entities. It specifies the output: which signal_id lines exist, entity counts, and freshness categories. This distinguishes it from sibling tools like get_official_record and verify_citation.
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 states this is free and can be used 'BEFORE paying', establishing a clear use case. It mentions optional filters, implying when to use them. However, it does not explicitly state when not to use this tool or suggest alternative tools for specific scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_official_recordGet the x402 payment challenge for a paid recordAInspectReturns the REAL x402 payment challenge (accepts[]: network / asset / payTo / amount) for the paid endpoint that serves a given signal_id+entity. This tool does NOT deliver paid data and does NOT take payment - MCP has no payment layer. Pay at the returned url with your own x402 client (USDC on Base or Solana, gasless EIP-3009; pay from a plain EOA) and you receive the record directly, with record_hash you can verify offline. This tool only fetches the 402 challenge; it delivers no paid data and collects no payment.
| Name | Required | Description | Default |
|---|---|---|---|
| dim | No | which product: full read (gauge, $0.05) or a single add-on | |
| entity | Yes | ||
| signal_id | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully discloses that the tool does not deliver paid data or collect payment, returns a challenge with specific fields, and describes the payment mechanism. It is transparent about its limited role.
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, starting with the primary output, then clarifying limitations, and concluding with payment guidance. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the tool's purpose, output, and follow-up actions. Missing an output schema is partially mitigated by mentioning the challenge fields. It could include error cases, but overall adequate 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?
Schema description coverage is low (33%); the description adds context that signal_id and entity identify the record, and dim selects the product. However, it does not provide detailed format or constraints for signal_id and entity, missing an opportunity to compensate 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 returns the x402 payment challenge for a paid endpoint, specifying what it does and does not do. It distinguishes itself from sibling tools (find_signal, verify_citation) by focusing on payment challenge 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?
The description explains when to use the tool (to get the challenge before paying) and provides steps after use (pay with x402 client). 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.
verify_citationVerify a Truth Bear record (free)AInspectFREE. Given a record_hash from any Truth Bear record, look it up and recompute the canonical hash server-side, returning whether it is a genuine Truth Bear official record plus a plain-language reverse lookup of what exactly that hash attests. Use this to check a citation you were handed by another agent or document BEFORE relying on it. No payment, no API key.
| Name | Required | Description | Default |
|---|---|---|---|
| record_hash | Yes | sha256:<64 hex>, or a >=8-hex prefix as posted publicly |
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 operation is free and requires no payment or API key, which is useful for an agent. It implies a read-only verification by stating it recomputes the hash and returns genuineness. However, it doesn't explicitly state it has no side effects or describe the return format in detail.
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, front-loading the key fact that it's free, then explaining the process and use case. Every sentence adds value, and there is no redundancy. Ideal length for this simple tool.
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 one parameter and no output schema or annotations, the description is fairly complete. It covers purpose, usage, and cost. However, it could elaborate on error handling or exact return structure, but the simplicity of the operation makes this adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage with a detailed description of the record_hash parameter (sha256:<64 hex> or prefix). The description adds minimal additional meaning beyond restating 'record_hash from any Truth Bear record'. 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 takes a record_hash, recomputes the hash server-side, and returns whether it's a genuine record plus a plain-language reverse lookup. It distinguishes itself from siblings (find_signal, get_official_record) by specifically targeting citation verification.
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 to use this when checking a citation from another agent or document. It emphasizes that it's free with no payment or API key. It doesn't explicitly mention when not to use, but the context implies alternatives exist.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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{
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