Benchmark MCP tool selection with metadata-only routing, collision, abstention, and holdout checks.
Read an existing public scan report by its 16-character id; no new scan, model call or target request.
| Name | Required | Description | Default |
|---|---|---|---|
| report_id | Yes |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral disclosure burden. It explicitly states that this is a read-only operation with no new scan, model call, or target request, and adds that the report must be public. It does not mention error behavior or rate limits, but for a simple retrieval tool this is reasonably transparent.
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, front-loaded sentence that immediately states the action, target, and key constraint. It contains no filler or redundant information and earns every word.
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 tool with one required parameter and an output schema provided, the description is complete enough. It tells the agent what the tool does, what input it needs, and what side effects it avoids. There are no meaningful gaps that would prevent correct selection or 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 meaning for the report_id parameter. It does so by identifying it as the id of an existing public scan report and noting its 16-character length, which complements the schema's pattern constraint. This is sufficient for the single parameter.
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 ('Read') with a clear resource, an existing public scan report, and identifies the key identifier format (16-character id). It also explicitly distinguishes itself from scan-generating tools by stating that no new scan, model call, or target request occurs.
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 clarifies when this tool is appropriate: when you want to read an existing public report by id. It also implies when not to use it by stating that it performs no new scan, model call, or target request, though it does not explicitly name alternatives or list exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a free public report of MCP tool-description routing collisions.
Reads discovery metadata (initialize/tools/list) only; never calls target business
tools or spends funds. Use a public HTTP(S) URL without credentials, query or fragment.
Results use at most 12 tools and 24 generated cases, not a real-model evaluation.
Reports persist and are public by link. For an existing report use get_selection_report.
Set internal_test=true for owner/CI validation so it is excluded from public scans.
source is a self-reported referral bucket, not identity verification.
| Name | Required | Description | Default |
|---|---|---|---|
| source | No | other | |
| mcp_url | Yes | ||
| internal_test | No |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so exceptionally: it discloses that the tool only reads discovery metadata, never calls business tools or spends funds, is limited to 12 tools/24 cases, persists reports publicly, and treats source as non-verifying.
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?
Every sentence contributes unique operational information: purpose, safety, URL constraints, limits, persistence, sibling routing, and testing flag. The description is front-loaded with the core purpose and then adds necessary caveats without repetition or filler.
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 output schema exists, so return-value documentation is not the description's burden. Given the tool's complexity, the description covers safety behavior, input constraints, limitations, persistence semantics, and sibling alternatives. An agent has enough context to invoke it correctly.
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 explain parameters itself. It does: mcp_url is a public HTTP(S) URL without credentials/query/fragment, internal_test excludes the scan from public results, and source is a self-reported referral bucket. All three parameters are meaningfully clarified.
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 opens with a specific verb and resource: 'Create a free public report of MCP tool-description routing collisions.' It clearly distinguishes this from sibling get_selection_report by stating that existing reports should be retrieved there instead. The scope is 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?
The description gives explicit input constraints: public HTTP(S) URL without credentials, query, or fragment. It also tells the agent when to use the sibling tool get_selection_report and when to set internal_test=true, providing concrete routing guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get free service capabilities, limits, privacy and connection details; does not scan a target.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the transparency burden. It clearly signals a read-only informational operation by using 'Get' and explicitly stating that it 'does not scan a target.' For a zero-parameter info tool this is sufficient behavioral disclosure; it does not create misleading expectations about side effects.
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, front-loaded sentence that conveys both the tool's purpose and its key exclusion ('does not scan a target'). Every word earns its place, and the semicolon cleanly separates the positive capability from the negative scope.
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 informational tool with an output schema available, the description fully covers selection and invocation. It states what the tool returns, clarifies that it is not a scan operation, and needs no additional setup, prerequisites, or parameter guidance.
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 tool has zero parameters, so the schema already fully defines the invocation surface. The description adds contextual meaning about what the tool reports, which is the only meaningful semantic contribution possible for a parameterless tool.
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 identifies the tool as an info-gathering call: 'Get free service capabilities, limits, privacy and connection details.' The appended 'does not scan a target' actively distinguishes it from scan-oriented sibling tools, so an agent can tell it apart without inspecting 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 when to use the tool: when you need service capability, limit, privacy, or connection information. However, it does not explicitly name alternatives or state 'use get_selection_report when...' or 'use scan_mcp_metadata when...' The negative clause hints at the boundary but leaves routing to the agent's inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
get_selection_reportscan_mcp_metadataselection_lab_infoClaiming 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, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
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. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. 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. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
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
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
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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Closed-source remote MCP: model benchmarks, costs, HN signals, tech registry.
Reproducible benchmarks and reliability evidence for agent tools.
Benchmark-first release surface with a read-only MCP endpoint and operator CLI.
Vouch — independently measured reliability scores for MCP tools, not self-reported claims.
Add one secure layer between your agents and this server.
Each tool serves a clearly distinct function: creating a scan, retrieving a report, and getting service information. The descriptions explicitly cross-reference the report retrieval path, leaving no boundary ambiguity.
Most names follow a clear verb_noun pattern (get_selection_report, scan_mcp_metadata), but selection_lab_info is a noun phrase rather than an action-oriented name. All names are snake_case and readable, so the deviation is minor.
Three tools is an appropriate scope for this service: scan creation, report retrieval, and service info. Each tool serves a distinct user need without redundancy or bloat.
The tool surface covers the full user journey: learn about the service, create a scan, and later retrieve the resulting report. No update/delete/list operations are needed because reports are public and effectively immutable, and service details are covered by selection_lab_info.
