entity-resolve
Server Details
Fuzzy entity resolution and dedupe for names, addresses, and company records. $0.02/call via x402.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Tool Definition Quality
Average 4.3/5 across 1 of 1 tools scored.
With only one tool, there is no risk of confusion or overlap with other tools.
The single tool is named 'resolve', which is clear and descriptive of its function, and there is no inconsistency.
One tool for entity resolution is borderline; while it is self-contained, typical use cases might benefit from additional tools for configuration or inspection.
The tool covers the core entity resolution workflow (deduplication, scoring, merging) but lacks customization options for thresholds or blocking parameters, which are minor gaps.
Available Tools
1 toolresolveAInspect
Fuzzy-dedupe a list of records into clusters of likely-duplicate entities. Blocks by normalized token prefix, scores with Jaro-Winkler + token-set matching (exact on email/phone), unions matches above threshold, and returns a merged canonical record per cluster with a confidence score. Deterministic, no LLM calls.
| Name | Required | Description | Default |
|---|---|---|---|
| options | No | ||
| records | Yes |
Tool Definition Quality
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description fully discloses the algorithm: blocking by normalized token prefix, scoring with Jaro-Winkler and token-set matching, exact matching on email/phone, union above threshold, and returning merged canonical records with confidence scores. No annotations are present, so this covers the behavioral burden.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences: first sentence states purpose, second details the algorithm. It is concise, front-loaded, and every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema and 0% schema description coverage, the description explains the algorithm but omits the output format details and does not explain all parameters (e.g., 'keys'). It is somewhat incomplete for a data-processing tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description references the 'threshold' parameter indirectly but does not explain the 'keys' parameter or the structure of 'records' and 'options'. With 0% schema description coverage, the description should provide parameter-level details; it falls short.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Fuzzy-dedupe a list of records into clusters of likely-duplicate entities.' The verb 'dedupe' and the resource 'list of records' are specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on how the tool works (blocking, scoring, merging) and notes it is deterministic with no LLM calls, hinting at when to use. However, without sibling tools, it does not explicitly differentiate from alternatives or state when not to use.
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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{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
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The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
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If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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