Entity Resolve
Server Details
Fuzzy entity resolution and dedupe for names, addresses, and company records — the "is this the same person/company" problem that breaks exact-match joins. Clean CRM exports, merge duplicates, reconcile vendor lists. Pay-per-call via x402 (USDC on Base): $0.008/call, no account or API key. tools/list and /openapi.json are free for discovery.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
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Full call logging
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Managed credentials
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Usage analytics
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Tool Definition Quality
Average 3.9/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusion or overlap.
A single tool named 'resolve' is inherently consistent with itself.
A single tool for a dedicated deduplication function is borderline thin; while it serves a focused purpose, most servers have 3-15 tools.
The tool covers the core deduplication task but lacks supporting operations like configuration or manual override, leaving some gaps for a full workflow.
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?
With no annotations provided, the description fully discloses the algorithm steps: blocking by token prefix, scoring with Jaro-Winkler and token-set matching, exact matching on email/phone, union above threshold, and output of merged canonical records with confidence. It also clarifies determinism and absence of LLM calls, covering the behavioral aspects well.
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 a single, coherent paragraph of three sentences that efficiently conveys purpose and algorithm. It front-loads the core idea but could benefit from bullet points or clearer separation of details for enhanced readability. It is concise without being overly terse.
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?
The tool is moderately complex (dedup algorithm, nested params). The description explains the algorithm well but omits details about the input record format, output structure (beyond 'merged canonical record with confidence'), and potential edge cases or errors. Given no output schema, these gaps reduce completeness.
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 input schema has 0% description coverage for parameters. The description does not explain the 'records' structure or the 'options.keys' and 'options.threshold' semantics, only briefly mentioning them contextually. This leaves agents to infer parameter meaning from the schema types alone, which is insufficient.
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 explicitly states it is a fuzzy-deduplication tool that clusters duplicate records and returns merged canonical records with confidence scores. It details the algorithms (Jaro-Winkler, token-set matching, email/phone exact match) and behavior, leaving no ambiguity about the tool's purpose.
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 implies the tool is for deduplicating records and mentions it is deterministic with no LLM calls, but it does not explicitly state when to use versus alternatives (e.g., other dedup approaches) or provide prerequisites or limitations. With no sibling tools, this level of guidance is adequate but not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$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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For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
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For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
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
The URL of the server is wrong
Credentials required to access the server are missing or invalid
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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