StatePulse API

requestMissingData

FREE endpoint. If your agent needs live data this API does not serve yet — a feed, a metric, a lookup, another city or chain — describe it here. Requests are reviewed and frequently-requested data becomes a real endpoint, usually within days; re-fetch openapi.json or llms.txt to see new endpoints. Submitting a request directly helps your agent: the data you ask for is prioritized so it exists the next time you need it. Matches: data not available, missing endpoint, request new data source, feature request, no endpoint for this, wish this API had.

Input Schema

TableJSON Schema

Name	Required	Description
`category`	No	Rough category: finance, blockchain, transit, environmental, network, social, other
`agent_wallet`	No	Optional EVM wallet of the requesting agent
`agent_contact`	No	Optional contact (URL, email, or handle) to be notified when the endpoint ships
`example_query`	No	An example request your agent would send to such an endpoint
`max_price_usd`	No	The most your agent would pay per call for this data, in USD (helps prioritization)
`requested_data`	Yes	Describe the live data you need (what it is, which source if known, how fresh it must be)

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are provided, so the description carries full burden. It explains that requests are reviewed, frequently-requested data becomes a real endpoint 'within days', and that re-fetching is needed to see new endpoints. It also highlights the prioritization benefit. Missing details like response format or rate limits are minor for a submission endpoint.

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 a single paragraph of four sentences, starting with 'FREE endpoint' for immediate clarity. It includes a 'Matches:' list for quick matching. While concise, some repetition (e.g., 're-fetch openapi.json' mentioned twice) trims efficiency slightly.

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?

Given that there is no output schema and the tool is for submitting a request, the description sufficiently covers purpose, usage, and expected outcomes. It explains how submissions are handled and prioritized, which is adequate for an agent to correctly invoke the tool.

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%, so the schema fully documents all six parameters. The description adds value by providing usage context ('Matches: data not available...') and emphasizing the `requested_data` field, but does not significantly extend the schema's descriptions.

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

Purpose5/5

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

The description clearly states the tool is for requesting missing data from the API. It uses specific verbs like 'describe it here' and 'submit a request'. It distinguishes from the many data retrieval siblings by being the sole endpoint for proposing new data sources.

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?

The description explicitly says 'If your agent needs live data this API does not serve yet' as the trigger. It advises re-fetching openapi.json to see new endpoints. While it doesn't name alternative tools, the context implies using existing endpoints when available, and the sibling list reinforces this.

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

B3/5.0

Disambiguation2/5

Many tools have overlapping functionality, e.g., auditNetworkHost combines DNS, SSL, and header checks that have dedicated tools (auditDnsSecurity, checkSslExpiry, auditSecurityHeaders). Multiple weather and blockchain tools also overlap in scope, making it difficult for an agent to choose the right tool.

Naming Consistency4/5

Most tools follow a consistent verb_noun pattern (e.g., getAirQuality, checkDnsPropagation), but a few deviate (agentPreflight, capabilitiesDiff) and some use compound names (depositCoordinationBounty). Overall, the pattern is clear but not perfectly uniform.

Tool Count1/5

With 56 tools, the server is far too large for a coherent MCP surface. The number suggests a collection of many unrelated APIs rather than a focused tool set. A typical well-scoped server has 3-15 tools.

Completeness2/5

While the tool set covers many domains, each domain has shallow coverage. For example, blockchain tools miss basic transaction sending and contract deployment; weather tools lack forecasts. The 'requestMissingData' endpoint acknowledges gaps, but the current surface is severely incomplete for a general-purpose API.

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