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data_changelog

Dated record of every change to the data this server sells: rule flips, label fixes, duplicate removals, band versions, sample notices, each with the date, the tools affected and the count. Optional tool name to filter, and limit

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNo
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
toolNo
countYes
errorNo
entriesYes
updatedNo
disabledNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

B3.3/5.0
Behavior3/5

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

With no annotations present, the description carries the transparency burden. It does disclose that results include the date, affected tools, and count, and that filtering and limiting are optional. It does not state ordering, default limit behavior, or explicitly confirm that the operation is read-only, though 'record' strongly implies a non-mutating query.

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 compact and front-loaded: the core purpose comes first, followed by concrete examples and then the parameters. The trailing 'and limit' fragment is slightly awkward, but the description avoids unnecessary words and earns its place.

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

Completeness4/5

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

For a simple filtered-read tool with an output schema, the description covers the essential content—dates, affected tools, and counts—as well as the optional filtering behavior. It lacks order and default-limit details, but those are minor gaps given the low complexity and the presence of an output schema.

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

Parameters2/5

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

The schema has zero property descriptions, so the description must compensate. It clarifies that 'tool' is an optional filter by tool name, but the limit parameter is only mentioned as 'and limit' without explaining that it controls result count, its default value, or any maximum. This is not enough to fully compensate for 0% schema coverage.

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 clearly identifies the tool as the dated changelog for the data this server sells, and it lists concrete change types such as rule flips, label fixes, and duplicate removals. It lacks a direct action verb like 'list' or 'return,' and it does not explicitly contrast itself with sibling tools, but the resource and scope are specific enough to be unambiguous.

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?

The usage context is implied: call this tool when you need a historical record of data changes, optionally filtered by tool name and limited in count. However, it provides no explicit when-to-use/when-not-to-use guidance and does not name any alternative tools, so the guidance is only implicit.

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

C2.9/5.0
Disambiguation2/5

Several tools occupy nearly the same territory: market_state, situation_report, and deep_report all present overlapping per-chain market analysis at different comprehensiveness levels. attest_spec and proof_spec are also near-identical, and attest_status, attestation_stats, and list_attestations use similar attestation vocabulary. Most data tools have unique jobs, but the overlapping report and verification tiers create real misselection risk.

Naming Consistency3/5

The server consistently uses snake_case but mixes noun-style data endpoints like prices and wall_map with command-style actions like attest, buy_key, and verify_reveal. Report names are also uneven: market_state, situation_report, and deep_report signal only vague depth differences. The set is readable but does not follow a single predictable convention.

Tool Count2/5

With 29 tools, this exceeds the heavy threshold and spans roughly four distinct functional areas: market data, attestation, key management, and webhooks. Each tool may earn its place individually, but the overall menu is too large for one MCP server; splitting it into data and attestation/administration servers would be clearer.

Completeness4/5

The attestation lifecycle is well covered with creation, status, listing, reveals, stats, and independent verification, and the key purchase and webhook workflows are also complete. The main gaps are historical-depth data, explicit key revocation, and a direct hire action despite hireable agents being listed, but agents can generally work around these. Core workflows do not dead-end.

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