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cc0company-mcp

by io.github.cryptomfer
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cc0-daily-brief

Hourly-refreshed top-5 CC0 NFT collections digest. Returns synchronous JSON with per-collection 24h metrics (volume, sales, floor, holders), cc0pedia editorial context (creator, summary, lore link), and an LLM-synthesized narrative + macro headline. Sources: OpenSea v2 stats, cc0pedia, Anthropic Haiku for synthesis. Cache TTL: hourly. Output license: CC0 — agents may reuse, repost, remix the JSON freely. [PAID: $0.05 USDC per call via x402]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
formatNoOutput format. Only 'json' is supported today; 'markdown' is planned for v2 to power newsletter pipelines without an extra LLM call.json

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changed
    • changedInput schema / additionalProperties
      Previous value: -trueNew value: +false
    • addedInput schema / description
      Added value: +"POST with `{}` (default) or `{ \"format\": \"json\" }`. The brief is precomputed hourly — the same payload is served until the next refresh."
    • addedInput schema / properties / format
      Added value: +{
+  "default": "json",
+  "description": "Output format. Only 'json' is supported today; 'markdown' is planned for v2 to power newsletter pipelines without an extra LLM call.",
+  "enum": [
+    "json"
+  ],
+  "type": "string"
+}
    • addedInput schema / required
      Added value: +[]
  2. First observed

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly: synchronous JSON return, hourly cache TTL, the exact upstream sources, an LLM-synthesis step, output licensing (CC0 reuse/remix), and a paid-call cost of $0.05 USDC via x402. These are exactly the traits an agent needs before invoking a paid, cached tool.

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?

It is front-loaded with the core purpose and then layers return shape, sources, cache policy, license, and price without filler. The information density is high and every clause carries signal, though the run-on list format makes it slightly dense to parse.

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

Completeness5/5

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

With no output schema and no annotations, the description compensates by detailing the return contents, the precomputed/cached behavior, the data sources, the payment model, and reuse licensing. An agent has everything needed to decide whether to call it and to interpret the response.

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% with a single optional 'format' enum, so the schema already documents the parameter fully, including the v2 markdown note. The description's 'Returns synchronous JSON' restates the default rather than adding syntax or format semantics beyond the schema. Baseline 3 is appropriate.

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 states a specific resource and scope: an hourly-refreshed top-5 CC0 NFT collections digest, with the payload contents enumerated (24h metrics, cc0pedia editorial context, synthesized narrative). This is far more than a restated name. It does not explicitly name or contrast with the closest sibling (cc0pedia-market), so the differentiation from siblings is implied rather than stated.

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

Usage Guidelines3/5

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

Usage is implied rather than guided: an agent can infer 'call this when you want a curated NFT digest.' The line about the payload being precomputed and unchanged until the next refresh usefully discourages repeated calls. However, no alternative tool (e.g. cc0pedia-market for raw metrics) is named, and no explicit when-not-to-use condition is given.

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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