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air-letters

GET https://ticks.bnm.farm/air-letters — $0.05 USDC on Base to 0xf59621FC406D266e18f314Ae18eF0a33b8401004. USDA APHIS AIR confirmation-letter text GET ?id= is one official text ($0.02). Newest chunk on a plain GET ($0.05); older chunk if they ask (?before, $0.05). Unpaid returns HTTP 402. After a valid X-PAYMENT, the same URL returns JSON. Not a new SKU.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoOfficial catalog id. That one official text, $0.02. Same door, not a new SKU.
pageNo1-based page. Page 1 is the newest chunk. Ignored when before is set.
beforeNoOfficial catalog id or YYYY-MM-DD. Next older chunk on the same URL, another $0.05. Omit for the newest chunk.
x_paymentNoOptional x402 X-PAYMENT value forwarded to the paid GET as the X-PAYMENT header.

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, this description carries the behavioral burden and mostly meets it: it discloses pricing, HTTP 402 when unpaid, that a valid X-PAYMENT switches the same URL to JSON, and the chunk/page semantics. It does not describe response structure or other error/rate-limit behavior, but the critical payment-dependent behavior is transparent.

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

Conciseness4/5

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

The description is dense and front-loaded with the URL and price; every main clause carries operational information. Some redundancy with the input schema ('Not a new SKU', newest-chunk statements) and the unstructured comma-dense style keep it from a 5.

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

Completeness3/5

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

For a paid, multi-mode endpoint with no output schema, it covers cost, auth condition, and pagination modes. However, it never describes the returned JSON shape or what a 'chunk' contains, nor gives an example, so an agent still has some uncertainty about the response it will need to handle.

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

Parameters3/5

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

Schema coverage is 100% and each parameter already has a detailed description including cost and chunk behavior, so the baseline applies. The tool description mostly restates those parameter semantics (e.g., ?id= official text, ?before= older chunk) rather than adding new meaning, though it does connect them to the endpoint and confirms the plain-GET default.

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

Purpose4/5

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

The description identifies a specific resource ('USDA APHIS AIR confirmation-letter text') and an HTTP GET verb, so an agent can connect the tool to its content area. It is distinguishable from siblings like warning-letters and untitled-letters by naming the AIR program, though the wording is terse and the 'chunk' vs 'official text' distinction slightly blurs the primary purpose.

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

Usage Guidelines4/5

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

It gives explicit call patterns: plain GET for newest chunk, ?id= for a single official text, ?before= for older chunks, and notes the cost difference. It does not explicitly compare against sibling tools, but the conditions for the tool's own modes are clear enough to select the right invocation.

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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TDQS

B3.4/5.0
Disambiguation3/5

Most tools map cleanly to distinct regulatory document collections (FDA, EPA, FDIC, etc.), so the majority are unambiguous. However, 'search' and 'firm-check' both offer free search with overlapping scope, and 'get-one' vs 'get-page' are easily confused payment operations. Similar prefixed names like mariners, mariners-d7/d8/d11 and gmp/gmp-md add further boundary fuzziness.

Naming Consistency3/5

The bulk of tools follow a consistent lowercase-hyphenated source-slug convention (aaib-reports, cder-reviews, cfpb-orders), which is predictable. But utility tools break the pattern with imperative/descriptive names (search, firm-check, get-one, get-page), and 'ticks' is a bare noun that doesn't fit either style. The mixed conventions are readable but not uniform.

Tool Count2/5

At 46 tools, this is a heavy surface that will be unwieldy for an agent to navigate and select from. Each tool does represent a distinct data product, but the sheer volume exceeds what earns its place in a coherent MCP tool set. A door-based catalog design might work better with a smaller number of parameterized tools.

Completeness4/5

The core workflow is well covered: per-door search, cross-door search, single-document retrieval via ?id=, paginated page retrieval, and payment handling are all present. Minor gaps exist, such as no tool to enumerate all available doors/catalogs or inspect schemas, and firm-check returns only citations rather than bodies. Overall it's a complete access lifecycle for the data-shop domain.