Screens public GitHub repos and PRs to generate risk maps, findings, and merge-readiness signals.
Claiming proves that you control this connector and unlocks listing details, thumbnails, health checks, and analytics. It does not change or interrupt the running server.
Complete one method below. Compare the methods and read troubleshooting steps.
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Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 3.8/5 across 5 of 5 tools scored.
Each tool serves a clearly distinct purpose: explaining the workflow, fetching repository structure, generating a report, and retrieving/listings existing reports. The overlap between fetching structure and screening is explicitly disambiguated by the 'without generating a full risk report' phrasing.
All tool names follow a consistent snake_case verb_noun pattern: explain_, get_, get_, list_, screen_. The verbs clearly map to their actions, and the objects are specific and readable.
Five tools is well-scoped for this server's purpose: generate, retrieve, list, inspect, and explain. Each tool earns its place without redundancy or bloat.
The core workflow of generating and retrieving risk reports is fully covered, with repository structure inspection as a useful supporting action. A delete/remove operation for locally stored reports is a minor missing feature but not a critical gap in the primary workflow.
explain_gitlumen_mcp_flowAInspectExplain how this Path 1 MCP server fits into GitLumen and how it later connects to Base MCP Path 2.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. 'Explain' clearly signals an informational, non-mutating action with no side effects, which is the core behavioral trait an agent needs. It does not specify whether the explanation is static or dynamically generated, but for a zero-parameter explanatory tool this is a minor omission, not a dangerous gap.
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 sentence that front-loads the verb and clearly states both parts of the scope (fit into GitLumen, later connection to Base MCP Path 2). It contains no fluff and does not repeat information already available in the name or schema.
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?
For a zero-parameter, no-output-schema tool that exists solely to explain a concept, this description is essentially complete: it tells the agent what the tool explains and how the explanation is framed. A note about the form of the output (e.g., prose explanation) would be slightly helpful but is not necessary for correct 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?
The input schema is empty and there are zero parameters, so there is nothing for the description to add over the schema. The baseline of 4 is appropriate because no parameter documentation is needed or expected.
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 uses a specific action ('Explain') and identifies exactly what is explained: how this Path 1 MCP server fits into GitLumen and connects to Base MCP Path 2. It is clearly distinct from sibling tools like get_repository_structure and get_review_report, which focus on data retrieval rather than conceptual explanation. It does not explicitly name a sibling, but the resource and action make the purpose 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?
There is no explicit when-to-use statement, exclusions, or alternatives. The imperative 'Explain...' implies the tool is appropriate when a user asks about the GitLumen MCP architecture or Path 2 connection, but the decision is left to inference. Since the sibling tools are not plausible alternatives for an explanation request, the absence of alternation guidance is not severe, but the guidance is still mostly implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_repository_structureAInspectFetch public GitHub repository or PR structure without generating a full risk report.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum tree entries to return. | |
| branch | No | Optional branch/ref. | |
| repoUrl | Yes | GitHub repository or pull request URL. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of behavioral disclosure. It indicates a read-only public fetch and clarifies that no risk report is generated, but it omits details about rate limits, truncation due to the 'limit' parameter, or error behavior for invalid/private URLs.
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 efficient sentence with no filler. The core action and the distinguishing negative clause are front-loaded, and every word earns 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?
No output schema exists, and the description does not explain what the returned 'structure' looks like, how truncation works with 'limit', or what happens on errors. The complete parameter schema helps, but the absence of return-value or edge-case information leaves the definition incomplete for an agent.
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%, so the baseline is 3. The description adds only that the repository is 'public', which slightly qualifies repoUrl, but it does not enhance understanding of 'limit' or 'branch' beyond the schema's existing parameter descriptions.
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 uses a specific verb, 'Fetch', and names the resource, 'public GitHub repository or PR structure', making the action clear. The clause 'without generating a full risk report' explicitly distinguishes it from sibling report-generation tools.
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 when only repository/PR structure is needed and explicitly says it does not generate a full risk report. However, it does not name alternative tools or provide explicit when-not-to-use guidance, so it stops short of a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_review_reportAInspectGet a previously generated GitLumen MCP report by reportId.
| Name | Required | Description | Default |
|---|---|---|---|
| output | No | compact | |
| reportId | Yes | Report id returned by screen_repository, for example glr_abcd1234abcd1234 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full behavioral burden. It clearly signals a read-style retrieval of an existing report and rules out generation, but it does not disclose failure behavior for missing/invalid reportIds or describe the response format. For a simple getter, this is adequate but not rich.
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 sentence with no filler. It front-loads the action, resource, and key parameter, making every word useful.
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?
For a straightforward get-by-id tool, the description plus schema cover the core information needed to select and invoke it: what it retrieves, the required ID, and the output format options. It only lacks explicit guidance about using list_review_reports to discover report IDs, which is a minor gap.
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 already documents reportId well, including an example and its source, and the output parameter is self-describing through its enum and default. The description itself adds little beyond restating 'by reportId' and does not explain the meaning or trade-offs of the output options.
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 states a specific verb ('Get') and a clear resource ('previously generated GitLumen MCP report'), keyed by reportId. The phrase 'previously generated' distinguishes it from generation-focused siblings like screen_repository without requiring the agent to inspect schemas.
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 this should be used after a report has been generated, since reportId comes from screen_repository. However, it does not explicitly mention sibling alternatives like list_review_reports for discovering report IDs, nor does it state when this tool should not be used.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_review_reportsAInspectList previously generated GitLumen MCP reports stored locally.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the only behavioral signal. It does disclose that the operation is local and that it only surfaces previously generated reports, implying no new generation or remote scanning. It does not, however, describe result ordering, empty-state behavior, or whether the operation is purely read-only, which would be helpful without annotations.
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, tight sentence with no filler. Every word contributes meaning: 'previously generated,' 'stored locally,' and 'reports' all constrain the tool's behavior.
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?
For a simple list tool with one optional parameter and no output schema, the description covers the essential context: what is listed, where it comes from, and that it is limited to prior local report generation. Additional return-format details would be nice but are not critical for correct 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?
The schema has one optional 'limit' parameter with default, minimum, and maximum, but no additional description coverage. The parameter name plus constraints make its meaning reasonably clear, so the description does not need to restate it. Still, the description adds no value about how 'limit' affects the returned list.
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 uses a specific verb ('List') and names a precise resource ('previously generated GitLumen MCP reports stored locally'). This clearly separates it from siblings like get_review_report (fetching one report) and screen_repository (scanning a repository), so an agent can identify its role immediately.
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 wording implies the tool is for enumerating already-generated local reports, which hints that it would be used before get_review_report. However, it does not explicitly state when to prefer this over alternatives or mention any exclusions, leaving some routing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
screen_repositoryAInspectScreen a public GitHub repository or GitHub pull request URL and generate a GitLumen-style risk report. Supports repo URLs and /pull/ URLs.
| Name | Required | Description | Default |
|---|---|---|---|
| scope | No | Screening depth. quick downloads fewer files; standard downloads more files. | standard |
| branch | No | Optional branch/ref. Ignored for PR URLs unless GitHub needs fallback. | |
| output | No | Response format returned to the MCP client. | compact |
| repoUrl | Yes | GitHub repository URL, for example https://github.com/owner/repo or https://github.com/owner/repo/pull/123 | |
| maxFiles | No | Optional hard cap for files downloaded and scanned. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must carry full behavioral disclosure. It states the action and supported URL shapes, but does not disclose that the tool downloads/scans file contents, the read-only nature beyond 'screen', network/resource implications, or any constraints. This is a meaningful gap for a tool with side effects like file downloads.
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?
Two sentences, front-loaded with the core purpose, followed by supported URL formats. No filler; all sentences add value and the description is appropriately sized for a tool with a detailed schema.
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 combination of description and schema is enough to invoke the tool, but the description alone does not clarify how this relates to sibling tools, what a 'GitLumen-style risk report' contains, or the behavior/impact of downloads. With no output schema, a bit more detail about report output would improve 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?
Schema description coverage is 100%, so all five parameters (repoUrl, scope, branch, output, maxFiles) are already documented with types, defaults, enums, and a repository URL example. The description adds no parameter-level details, which is acceptable given the schema's rich 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 uses a specific verb ('Screen'), names the exact resource ('public GitHub repository or GitHub pull request URL'), and states the output ('GitLumen-style risk report'). This clearly differentiates it from siblings like get_repository_structure and list_review_reports.
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 when to use this tool (when a risk report on a public repo/PR is needed) but gives no explicit when-not-to-use guidance or alternatives. It does not mention that get_review_report/list_review_reports serve existing reports or that get_repository_structure covers structure-only requests. The 'public' qualifier is the only condition stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user or an account that owns the GitHub organization, then choose Claim with GitHub.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, bound to the signed-in Glama account, and expire after seven days. They contain no email address or other personal information. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
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.
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