Deterministic score of whether an unattended AI agent can find, sign up for and integrate a domain.
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 2 of 2 tools scored.
The two tools have completely distinct purposes: find_providers accepts a problem description and returns vendor evaluation data, while scan_domain accepts a domain and returns HTTP check results. There is no overlap in input, output, or functionality, so an agent can easily distinguish them.
Both tool names follow a clear verb_noun pattern with underscores (find_providers, scan_domain), which is consistent and predictable. There are no deviations or mixed conventions.
With only two tools, the server's surface is thin. While each tool is detailed and serves a clear purpose, the server name 'Let Agents In' suggests a broader scope, making the count feel borderline for the implied domain.
The server covers only two specific evaluation tasks, leaving obvious gaps for a server that aims to 'let agents in.' Expected tools for access management, onboarding, or broader agent orchestration are missing, making the surface incomplete for the stated purpose.
find_providersFind providers an unattended agent can actually finish withADescribe the problem in your own words, for example "let users upload images" or "send transactional email". Returns the vendors we have measured in that category, split by whether an unattended run clears every barrier we test, stops at one, or was never measurable, each with the date and a link to the evidence. This is not a recommendation: it does not know whether a vendor suits your job, only where an agent stops. Routing a sentence to a category is by far the weakest thing here, and the numbers below are the ones to plan around. Measured on 40 questions written by an agent with no access to this repository and no sight of the category list, labelled before the first run, and built to be hard: fourteen of them ask about the caller own code in commercial words (a billing module, a payments table, a notifications worker), and six ask for something real that a catalogue this size does not hold. It got 29 of the 40 right, said nothing on 8 it should have answered, sent 0 to the wrong category and answered 3 that it should have refused. Put another way: it gave an answer to 14 of the 40, and 3 of those answers were wrong, while refusing 18 of the 21 it should have refused. It is deliberately quiet. One vocabulary word inside a long question decides nothing, and a question shaped like a request for code rather than for a vendor is refused outright. Silence means we could not read the question, not that the category is empty. The tool reads English. Name the category yourself when you know it.
| Name | Required | Description | Default |
|---|---|---|---|
| job | Yes | The problem to solve, in your own words. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only and idempotent behavior, but the description adds substantial context: return format details, performance metrics from a benchmark, the deliberate quietness, refusal patterns, and the distinction between 'silence' and empty category. It goes far beyond what annotations provide and does not contradict them.
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?
While the description is front-loaded with the main instruction and return format, it quickly digresses into an extensive evaluation methodology discussion (40 questions, 29/40 accuracy, etc.). This is a wall of text that could be trimmed to the essential usage and behavioral notes. Every sentence does not earn its place.
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?
With no output schema, the description fully explains what the tool returns (vendors with date and evidence link, split by barrier status). It also covers input expectations, limitations, and how to interpret silence. It is complete enough for an agent to use correctly, even if overlong.
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 schema has only a basic description for the 'job' parameter, but the tool description expands significantly: it gives examples, explains how to phrase problems, notes that English is expected, and clarifies how different input shapes are handled. This adds meaning well beyond the schema field.
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 function: it takes a free-text problem description and returns measured vendors in that category, split by barrier outcomes, with dates and evidence links. It explicitly distinguishes itself from a recommendation tool, clarifying its actual scope. The title and description are aligned.
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 explicit usage instructions: 'Describe the problem in your own words,' notes the tool reads English, and warns that silence means it could not read the question. It also specifies when not to use it ('This is not a recommendation') and suggests naming the category when known. This is comprehensive guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
scan_domainScan a domain for agent readinessAInspectScores a domain across 5 funnel stages using 16 deterministic HTTP checks. 18 points exist on paper; the score is out of the points that both applied to the domain and could be evaluated. Returns a per-check breakdown with the reason for each result and a permanent link.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Bare domain, for example example.com | |
| format | No | summary is the readable breakdown. agent returns markdown instructions you can act on directly, each task carrying the measurement behind it. sarif returns SARIF 2.1.0 for a code-scanning pipeline. | summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that the tool makes 16 deterministic HTTP checks, which aligns with the openWorldHint annotation indicating external side effects. It adds context about the scoring logic and that a permanent link is returned, going beyond the annotations. However, it could more explicitly state that HTTP requests are made to the submitted domain, but overall it is transparent enough for an agent to anticipate 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 three sentences, no filler, and front-loads the core purpose. Every sentence provides essential information: the scoring method, the scoring scale, and the output format. This is an appropriately sized and well-structured description.
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 tool has only 2 parameters, no output schema, and moderate complexity, the description covers the key aspects: what the tool does, how scoring works, and what the result includes (per-check breakdown with link). It is missing explicit mention of the external HTTP requests (implied by openWorldHint) which could affect agent planning, but still leaves agents reasonably informed for 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 100%, with both parameters (domain, format) fully described in the schema. The tool description does not add any additional semantic value beyond what the schema already provides for these parameters. Therefore, it meets the baseline expectation with no extra contribution.
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 scores a domain across 5 funnel stages using 16 deterministic HTTP checks. The verb "scores" is specific, the resource "domain" is explicit, and the explanation of the scoring system (18 points, scaled) distinguishes it from the sibling tool find_providers, which likely searches for providers rather than assessing readiness.
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 usage for assessing a domain's agent readiness, but it does not explicitly state when to use this tool versus find_providers or any other alternative. No guidance on exclusions (e.g., when not to use) or prerequisites is provided, leaving the agent to infer context from the purpose alone.
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!
