Superseded listing, not a dead server. This entry was imported from an early Glama registration under com.moltlinestudio.mcp; the same server is listed under its official MCP Registry name — use com.moltlinestudio/govern. Endpoint unchanged: https://mcp.moltlinestudio.com/govern — still live, still free on the free tier. Only this duplicate entry is deprecated.
Inventory and audit your agent fleet over MCP: audit_mcp_config returns risk-ranked findings with a 0-100 posture score, scope_check ranks tool-manifest blast radius, audit_skill_file and injection_scan screen untrusted skill files and text, and inventory_report builds a governance inventory with risk tiers. All analysis is deterministic and local to the request.
Score a public domain against 21 agent-readiness checks. FREE.
Use when you need to know whether an autonomous agent can discover, read, use or pay a website - your own, or a vendor you are evaluating before recommending it. Typical input {"domain": "example.com"} returns {"score": 8, "total": 21, "grade": "F", "passed": [...], "failed": [{"title": "...", "detail": "...", "fix": "..."}], "report_url": "..."} where report_url is a permanent shareable page for the same result.
Not for auditing an MCP client configuration (audit_mcp_config) and not for scanning text for injection (injection_scan) - this one reaches out over the network and fetches public URLs on a live domain. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""} (for example {"error": "The readiness scanner is not reachable right now."}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | A public hostname such as example.com. A full URL is accepted and reduced to its host. Hostnames that resolve to private or internal addresses are refused. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the description's job is to add beyond these. It adds critical behavioral details: the tool never raises a protocol error on invalid input (returns a structured error object), it reaches out over the network, it is safe to retry after correcting input, and the output includes a permanent shareable report URL. There is no contradiction with 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 well-structured with clear paragraphs: purpose, core use case, example output, sibling differentiation, and error behavior. Every sentence is non-redundant and adds unique value. It is front-loaded with the key verb and 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?
Given the tool has 1 simple parameter, rich annotations, and an output schema (indicated by context signals), the description covers everything: purpose, input handling, output structure, error behavior, idempotency, and safety guarantees. There are no gaps for an agent to misinterpret.
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 value by showing typical input/output examples and the error handling pattern, which clarifies how the domain parameter is used. It also adds the nuance that a full URL is accepted and reduced to its host. However, it does not add entirely new semantics beyond what the schema already documents.
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 ('Score') and clearly identifies the resource ('a public domain against 21 agent-readiness checks'). It also distinguishes itself from siblings by name in the usage guidelines section, and the title reinforces the purpose. There is no ambiguity about what the tool does.
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 states when to use the tool ('when you need to know whether an autonomous agent can discover, read, use or pay a website') and provides two specific exclusions by naming sibling tools ('Not for auditing an MCP client configuration (audit_mcp_config) and not for scanning text for injection (injection_scan)'). It also clarifies what it does differently ('this one reaches out over the network'). This is exemplary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Audit an MCP server config for risk-ranked posture findings. FREE.
Flags exposed machine credentials in the config, required inputs that aren't gated/optional, unpinned versions, over-broad env access, and dangerous auto-run flags. It never echoes any matched secret value back. Typical input {"config": "<mcpize.yaml, mcp.json, or a Claude/Cursor servers block>"} returns {"posture_score": 0-100, "verdict": "...", "findings": [{"line": N, "severity": 1-5, "issue": "...", "fix": "..."}], "note": "..."}.
Use on a server configuration document. Not for a skill or instruction file (audit_skill_file) and not for untrusted content an agent is about to read (injection_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| config | Yes | The MCP config to audit, pasted as text or JSON — mcpize.yaml, mcp.json, or a Claude/Cursor servers block. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint, idempotentHint, and destructiveHint=false. Description adds 'FREE', 'never echoes any matched secret value back', and reiterates 'Every call is read-only and idempotent'. No contradiction; description significantly supplements 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?
Moderately sized but well-structured: purpose first, then flag list, safety note, I/O example, usage guidance, error behavior, idempotency. Every sentence contributes; could possibly tighten the flag list but overall efficient for the detail provided.
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 output schema exists (context signals) and the single parameter is fully described, the description covers input format, output structure, error handling, behavioral safety, and appropriate usage context. No gaps remain.
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 already provides 100% coverage with a clear description of the 'config' parameter. The description adds a concrete example of typical input structure, which adds value beyond the schema but is not transformative. Baseline 3, adjusted to 4 for the useful example.
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?
Clearly states 'Audit an MCP server config for risk-ranked posture findings.' Lists specific flag types (machine credentials, required inputs, unpinned versions, etc.). Distinguishes from siblings audit_skill_file and injection_scan by naming them and their different targets.
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?
Explicitly states 'Use on a server configuration document. Not for a skill or instruction file (audit_skill_file) and not for untrusted content an agent is about to read (injection_scan).' Also explains error recovery ('never raises a protocol error...safe to retry').
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Audit an agent skill or instruction file before you trust it. FREE.
Checks for governance smells: prompt-injection and guardrail-bypass phrasing, concealment instructions ('don't tell the user'), exfiltration language, and exposed credential material. Typical input {"content": "<SKILL.md, system prompt, or tool description text>"} returns {"verdict": "reject — do not install" | "no governance red flags on a pattern pass", "findings": [{"severity": 1-5, "issue": "..."}], "note": "..."}.
Use before trusting a skill or instruction file that came from outside your own repository. Not for arbitrary untrusted input at run time (injection_scan). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Full text of the skill file, system prompt, or tool description to audit. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description amplifies this with detailed behavioral traits: no protocol error on malformed input, returns error object with remediation advice, retry-safe after correction. This adds context beyond what annotations convey.
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?
All text is front-loaded with clear purpose, then usage, behavior, and error handling. Every sentence adds unique value. No filler or repetition. The structure is logical and easy to scan.
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 an output schema exists, the description doesn't need to detail return values, but it still does succinctly. With only one parameter and full schema coverage, the description is complete for all agent decision-making needs—purpose, usage, behavior, error handling, and examples.
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 coverage is 100%, so baseline is 3. The description adds extra value by providing a working example of the input JSON ({"content": "..."}) and explaining what the content represents ('Full text of the skill file, system prompt, or tool description'), which goes beyond the bare schema description.
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 audits agent skill or instruction files for governance issues like prompt-injection and guardrail-bypass. It specifies the verb 'audit', the resource 'skill or instruction file', and distinguishes it from siblings like injection_scan which is for runtime input.
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?
Explicitly tells when to use ('before trusting a skill or instruction file from outside your own repository') and when not to ('Not for arbitrary untrusted input at run time (injection_scan)'). Provides an alternative sibling tool name, showing clear usage boundaries.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Load the Governance Auditor persona for consistent fleet audits. PREMIUM (license).
The persona is methodical, evidence-driven, and allergic to 'it's probably fine'. Takes no arguments. Returns {"persona": ..., "identity": ..., "rules": ["...", ...], "opening_move": "..."} ready to adopt as a system prompt.
Use to keep repeated audits consistent in voice and rigor. Not for running an audit - the audit tools do that. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| 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?
Annotations already declare readOnly and idempotent hints. Description adds significant behavioral detail: error handling behavior ('never raises a protocol error — it returns {error: ...}'), reinforces idempotency, and notes zero arguments. No contradictions.
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 adds value: purpose, license, persona traits, argument count, return format, usage context, error behavior. Front-loaded with main purpose. No wasted words.
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 zero parameters and existence of an output schema, the description covers return structure, error behavior, idempotency, and usage boundaries relative to siblings. No gaps for this simple tool.
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?
Zero parameters, schema coverage 100%. Description states 'Takes no arguments' which is sufficient. Baseline of 4 applies.
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?
Clearly states 'Load the Governance Auditor persona for consistent fleet audits' and specifies it takes no arguments and returns a persona object. Distinguishes from siblings like audit tools and scan 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?
Explicitly says 'Use to keep repeated audits consistent in voice and rigor. Not for running an audit - the audit tools do that.' Provides clear when-to-use and when-not-to-use guidance, plus mentions PREMIUM license requirement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate an audit-ready agent-governance policy for a fleet. PREMIUM (license).
Covers inventory cadence, ownership rules, least-privilege approval gates, injection defense, logging/retention, and decommissioning triggers. Typical input {"fleet_context": "20 agents, 3 with shell access, one finance bot"} returns {"policy": ..., "sections": {...}, "context_note": ..., "audit_checklist": ["...", ...]}.
Use when a fleet needs a written policy document. Not for assessing what the fleet currently does (inventory_report, audit_mcp_config). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| fleet_context | No | Optional plain-language description of the fleet (size, capabilities, sensitive systems) used to tailor the policy; empty returns the generic baseline. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds beyond these: error handling behavior (returns error objects instead of protocol errors), retry safety, and a licensing note (PREMIUM). These are valuable disclosures not present in structured fields.
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 and well-structured: purpose first, then coverage scope, then input/output example, usage guidelines, error behavior, and idempotency. Every sentence adds value without redundancy.
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 single-parameter optional tool with an output schema, the description covers all essential aspects: functionality, when to use, output example, error handling, and idempotency. No gaps remain given the context signals.
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 coverage is 100% with a clear description for the lone parameter. The description adds a concrete example of typical input, which helps the agent understand the expected format and tailoring. While the baseline is 3 due to high schema coverage, the example and context elevate the score.
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-resource pair ('Generate an audit-ready agent-governance policy for a fleet') and explicitly differentiates from siblings by stating what the tool is not for (inventory_report, audit_mcp_config).
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?
Clearly states when to use ('Use when a fleet needs a written policy document') and when not to use, naming specific alternative tools. This provides explicit guidance for agent decision-making.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Scan untrusted text for prompt-injection patterns before ingestion. FREE.
Use on any web page, email, or document an agent is about to ingest to catch prompt-injection and data-exfiltration patterns before they reach the agent's context. Typical input {"text": ""} returns {"injection_suspected": bool, "count": N, "hits": [{"line": N, "pattern": "...", "text": ""}], "note": "..."}.
Not for reviewing a skill file you control (audit_skill_file), and a clean result is not a guarantee of safety - it reports pattern matches only. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The untrusted content to scan, pasted as a single string. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable detail: error handling (returns error object instead of protocol error), and the fact that the tool is safe to retry after correction. It also describes the return structure, which goes beyond 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 efficiently structured: it starts with the core purpose, then adds usage context, output format, limitations, and error behavior. Every sentence adds value with no redundancy, and it is front-loaded with the most critical information.
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's simplicity (one parameter, output schema present) and comprehensive annotations, the description covers all aspects: when/why to use, input/output format, error handling, and limitations. It is fully adequate for an agent to use 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 100%, so baseline is 3. The description adds context by showing a typical input example and describing the output format, which helps the agent understand how to structure calls and interpret results.
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 explicitly states the tool scans untrusted text for prompt-injection patterns before ingestion. It clearly differentiates from the sibling tool audit_skill_file by specifying 'Not for reviewing a skill file you control'.
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 clear guidance: use on any untrusted web page, email, or document before ingestion. It also explicitly states when not to use (audit_skill_file) and warns that a clean result is not a guarantee of safety, setting proper expectations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Build a governance inventory with risk tiers from a raw agent list. FREE.
Turns a list of agents / MCP servers / skills into an audit-ready summary with critical/elevated/standard tiers and unowned-agent flags. Typical input {"items": "[{"name": "deploy-bot", "owner": "ana"}]"} returns {"total": N, "tiers": {"critical": N, ...}, "unowned_agents": [...], "inventory": [{"name": ..., "owner": ..., "tier": ..., "orphaned": bool}], "reading": "...", "note": "..."}.
Use to turn a raw agent list into risk tiers. Not for auditing any single agent in depth (audit_mcp_config, scope_check). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| items | Yes | The fleet as a string — a JSON array of {name, owner?, capabilities?, last_seen?} objects, or plain newline-separated agent names. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already signal read-only, idempotent, and non-destructive behavior. The description adds value by clarifying error behavior ('never raises a protocol error — returns an error object with fix instructions'), which is beyond what annotations provide. No contradiction between description and 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 well-structured with a one-line summary, then a detailed example, then usage guidelines and error handling. It is slightly verbose with the long example but still efficient for the information conveyed. Every sentence 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?
Given the single parameter, low complexity, and presence of output schema and annotations, the description covers purpose, usage, behavior, and error handling. It could briefly mention that the output schema defines the return structure, but it's near-complete.
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 coverage is 100%, so the baseline is 3. The description adds context by providing an example input and output shape, including the format for the 'items' parameter, which meaningfully augments the schema's description.
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 'build[s] a governance inventory with risk tiers from a raw agent list,' which is a specific verb+resource. It distinguishes itself from siblings like 'audit_mcp_config' and 'scope_check' which are for in-depth single agent auditing.
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 says when to use this tool ('Turn a raw agent list into risk tiers') and when not to ('Not for auditing any single agent in depth') while naming alternatives ('audit_mcp_config, scope_check'). It also mentions the tool is free and safe to retry on errors.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score the blast radius of every tool in a permission manifest. FREE.
Ranks each tool by capability risk (command exec > money/delete > file-write/messaging > read > network) and flags the over-privileged ones that need approval gates. Typical input {"tools": "["run_shell", "read_docs"]"} returns {"tools_scored": N, "high_risk_tools": N, "ranking": [{"tool": ..., "blast_radius": 0-5, "capabilities": [...]}], "recommendation": ["..."], "note": "..."}.
Use on a permission manifest to rank tools by blast radius. Not for the configuration that mounts them (audit_mcp_config). Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": ""}. Every call is read-only and idempotent, so after correcting the input it is always safe to retry.
| Name | Required | Description | Default |
|---|---|---|---|
| tools | Yes | The manifest as a string — a JSON array of tool names or {name, description} objects, a JSON object of name->description, or plain newline-separated names. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context beyond that: a specific error-handling contract ('never raises a protocol error — it returns {"error": ...}') and the risk ranking hierarchy. It repeats the read-only/idempotent traits as reinforcement but adds genuine new information.
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 front-loaded with the purpose, then provides a compact example, usage note, and error behavior. It is slightly long but every section earns its place; no filler words or redundancies beyond the intentional restatement of safety traits.
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 moderately complex tool that ranks blast radius, the description covers purpose, risk model, input format via example, output structure, error handling, and a usage exclusion. Even with an output schema present, the description is self-sufficient and leaves no major gaps for an agent to guess.
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% (the 'tools' param is fully described). The description adds a concrete typical input example and the exact output structure, which helps the agent understand how the parameter is used in practice and what to expect in the response. This goes beyond the schema without contradicting it.
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+resource combination ('Score the blast radius of every tool in a permission manifest'), and immediately differentiates itself from sibling audit_mcp_config by stating 'Not for the configuration that mounts them'. This leaves no ambiguity about the tool's function.
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?
It provides explicit usage guidance: 'Use on a permission manifest to rank tools by blast radius' and clearly excludes the alternative with 'Not for the configuration that mounts them (audit_mcp_config)'. This is exactly the kind of when/when-not guidance desired.
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.
agent_readiness_scanaudit_mcp_configaudit_skill_fileget_auditor_personagovernance_policyinjection_scaninventory_reportscope_checkClaiming 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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Add one secure layer between your agents and this server.
Every tool has a clearly distinct purpose targeting a different aspect of agent governance: domain readiness, config auditing, skill file auditing, injection scanning, inventory, blast radius scoring, policy generation, and persona loading. Each description explicitly states what the tool is not for, eliminating ambiguity.
Naming is inconsistent: some tools start with a verb (audit_mcp_config, get_auditor_persona), others with a noun (agent_readiness_scan, injection_scan, inventory_report, scope_check), and governance_policy is a plain noun phrase with no verb. There is no predictable pattern across the set.
8 tools is well-scoped for the domain of agent governance and auditing. Each tool serves a distinct and necessary function without redundancy, covering readiness, configuration, skill files, injection, inventory, permissions, policy, and persona.
The tool surface covers the major governance activities: audit, scan, inventory, policy generation, and blast radius. A minor gap is the lack of a tool to verify compliance against a generated policy, but the existing audit tools can be used for that purpose.