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.
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Managed credentials
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Usage analytics
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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.
resolveAInspectFuzzy-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 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully carries the burden of disclosure. It details the algorithm (blocking, scoring, matching) and states determinism and no LLM calls. However, it omits performance characteristics and error behavior, which prevents a perfect score.
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 concise at two sentences, with the purpose front-loaded. Every sentence provides essential information about the tool's function and behavior without unnecessary detail.
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 the lack of output schema and 0% schema description coverage, the description provides algorithm transparency but insufficient parameter semantics and no usage guidance. It is reasonably complete for a deduplication tool but could add more context.
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?
Schema description coverage is 0%, so the description must add meaning. It explains the 'threshold' parameter implicitly and the algorithm for matching, but does not detail the 'keys' parameter or the expected format of 'records'. This partially compensates but leaves gaps.
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 verb 'Fuzzy-dedupe' and the resource 'list of records', explicitly describing the tool's function: deduplicating records into clusters. With no sibling tools, no further differentiation is needed.
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?
There is no explicit guidance on when to use this tool versus alternatives, but the lack of sibling tools reduces the need. The description implies its use for deduplication tasks, but does not provide conditions, prerequisites, or when not to use it.
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.
Control your server's listing on Glama, including description and metadata
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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
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
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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