Fuzzy entity resolution and dedupe for names, addresses, and company records. $0.02/call via x402.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.4/5 across 1 of 1 tools scored.
Only one tool exists, so there is no possibility of confusion between tools. The single tool has a clear and distinct purpose.
With only one tool, naming consistency is trivially maintained. The verb-based name 'resolve' fits a common naming pattern.
A single tool for entity resolution is borderline; the server's scope is narrow, but 1 tool falls outside the typical 3-15 range and may feel thin for more complex workflows.
The single tool covers the entire entity resolution workflow (blocking, scoring, merging, returning canonical records) with no obvious gaps for its stated domain.
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 discloses the algorithm: blocking by normalized token prefix, scoring with Jaro-Winkler and token-set matching, exact match on email/phone, union above threshold, and deterministic output. This provides complete transparency about the tool's behavior.
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 long, packed with essential information about the tool's purpose and algorithm. It is front-loaded with the main action and does not contain any unnecessary words or details.
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 complexity of a dedup algorithm and no output schema, the description explains the return format (merged canonical record per cluster with confidence) and the major algorithmic steps. Minor missing details like error handling or constraints (maxItems) slightly reduce completeness, but the description is largely sufficient for agent invocation.
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 value. It refers to 'list of records' for the records parameter and mentions 'threshold' in the algorithm, but does not explicitly describe the 'keys' sub-parameter in options. The description adds moderate context beyond the schema, but lacks full param coverage.
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 performs fuzzy-deduplication of records into clusters of likely duplicates, returning merged canonical records with confidence scores. It provides specific details on the algorithm (blocking, scoring) that distinguish it from any potential generic dedup tool.
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 explicitly explains when to use the tool (for fuzzy dedup) and describes its algorithmic behavior. It lacks explicit exclusions or alternatives, but the deterministic and no-LLM-call nature gives clear context. With no sibling tools provided, the guidance is adequate.
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
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
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.
No comments yet. Be the first to start the discussion!