DataPulse

Check Cross-Source Reconciliation

check_reconciliation

Read-onlyIdempotent

Return the published cross-source reconciliation group for a dataset name or id, including per-member counts, dates, statuses, tolerances, and contextual deltas. A discrepancy requires human review and does not prove either source is wrong. Use it to compare a dataset with its published cross-source group; do not use it for provenance or evidence receipts—use get_provenance or get_evidence instead. It reads precomputed reconciliation data, and single_source means no group contains the resolved dataset; DataPulse is read-only, requires no API key, and the edge limits clients to roughly one request per second with a small burst, so pace or retry.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "dataset_name": "<dataset_name>"
    },
    "name": "check_reconciliation"
  }
}

Input Schema

Name	Required	Description	Default
`dataset_name`	Yes	Stable dataset slug or exact display name to resolve, e.g. 'interestrates' or 'Monthly Interest Rates'; use search_datasets to discover valid values.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedSep 21, 2026

changedInput schema / properties / dataset_name / description

Previous value: -"Dataset id or name to reconcile, e.g. 'interestrates' or 'Monthly Interest Rates'."New value: +"Stable dataset slug or exact display name to resolve, e.g. 'interestrates' or 'Monthly Interest Rates'; use search_datasets to discover valid values."

AddedAug 17, 2026

TDQS

A4.5/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond that: interpretation guidance ('does not prove either source is wrong'), the semantic meaning of 'single_source', the read-only/precomputed nature, and rate-limit behavior. Slight gap: no detail on what 'contextual deltas' means, but annotations carry the safety profile.

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 informative without being wasteful. The core purpose is front-loaded in the first sentence, followed by usage boundaries and behavioral caveats. It packs many distinct pieces of guidance into a compact space, though the last sentence is slightly long and could be split for readability.

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 a single parameter, 100% schema coverage, rich annotations, and an output schema, the description covers key context: purpose, alternatives, interpretation, read-only behavior, and rate limiting. It lacks a mention of pagination or potential error conditions, but these are minor given the output schema and simple parameter surface.

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

Parameters4/5

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

Schema description coverage is 100% and the schema already provides a clear description and examples. The description adds the resolution detail (slug or exact display name) by pointing to discoverability via search_datasets)Skip? It enriches the parameter meaning by tying it to the tool's purpose, though the schema already does most of the work.

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?

States a specific verb ('Return') and resource ('published cross-source reconciliation group'), and enumerates the returned content (per-member counts, dates, statuses, tolerances, contextual deltas). It also explicitly differentiates from provenance/evidence tools, making its purpose unambiguous against siblings.

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

Usage Guidelines5/5

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

Explicitly says when to use (compare a dataset with its published cross-source group) and when not to use, naming the alternatives (get_provenance or get_evidence). It also warns that discrepancies require human review)Skip? It explains interpretation caveat, read-only nature, and rate limiting.

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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