rust-faf-mcp
Server Details
Persistent project context — Rust-native MCP server. IANA-registered .faf format.
- Status
- Healthy
- Last Tested
- Transport
- Streamable HTTP
- URL
- Repository
- Wolfe-Jam/rust-faf-mcp
- GitHub Stars
- 4
- Server Listing
- rust-faf-mcp RMCP
Available Tools
3 toolsfaf_readCInspect
Parse a .faf and return its normalized structure (Rust→WASM, at the edge).
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Raw .faf file content (YAML) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must convey behavioral traits. It mentions 'normalized structure' but does not discuss side effects, performance, error behavior, or safety. The parse operation is likely read-only but unstated.
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 very short (one sentence) with no wasted words. However, it includes the parenthetical '(Rust→WASM, at the edge)' which is not essential for understanding the tool's function.
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?
Since no output schema exists, the description should elaborate on the return value beyond 'normalized structure'. It lacks details on error handling, encoding, or the structure format, making it incomplete for a parsing 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?
Schema coverage is 100% and the single parameter 'content' is described. The tool description adds no extra meaning beyond the schema's 'Raw .faf file content (YAML)'.
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 it parses a .faf file and returns a normalized structure, clearly indicating the verb and resource. It distinguishes from siblings (faf_score, faf_validate) by implying different functions, but the technology detail (Rust→WASM) is not essential for tool selection.
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?
No guidance on when to use this tool versus alternatives like faf_score or faf_validate. The description does not mention prerequisites, when-not-to-use, or context for invocation.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
faf_scoreBInspect
Score a .faf — 0-100 completeness (Rust→WASM, at the edge).
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Raw .faf file content (YAML) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description fails to disclose side effects, mutability, or constraints like idempotency or rate limits. 'Score' suggests a safe computation, but no confirmation.
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?
Single, front-loaded sentence with no waste. Efficiently communicates core purpose and context.
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?
Missing behavioral details, usage scenarios, and return format; with no output schema nor annotations, description is insufficient for a standalone 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?
Schema coverage 100%; parameter 'content' already described as 'Raw .faf file content (YAML)' in schema. Description adds no additional meaning beyond the schema.
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 verb 'Score' and resource '.faf' with specific output range '0-100 completeness' and context 'Rust→WASM, at the edge', distinguishing from sibling tools read/validate.
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?
No explicit when-to-use or alternatives provided; usage is implied but not contrasted with sibling tools or edge cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
faf_validateBInspect
Validate a .faf and return completeness score + errors/warnings (Rust→WASM, at the edge).
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Raw .faf file content (YAML) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses that validation runs via Rust→WASM at the edge, and that it returns diagnostic information (errors/warnings). However, without annotations, it does not state whether the operation is read-only, idempotent, or has side effects, which is moderately informative for a validation tool.
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?
A single concise sentence that immediately states the tool's purpose and key output. Every part contributes value with 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?
For a simple validation tool with one parameter and no output schema, the description is mostly complete: it states the action, input, and return type. Minor omissions like error handling or size limits exist but are not critical given the tool's straightforward nature.
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 covers 100% of parameters and already describes 'content' as 'Raw .faf file content (YAML)'. The description adds no further meaning, so it meets the baseline but does not exceed 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 clearly states the verb 'Validate' and the resource '.faf file', and specifies the output: completeness score plus errors/warnings. This distinguishes it from sibling tools 'faf_read' and 'faf_score' by the action performed, though it does not explicitly contrast them.
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?
No guidance is provided on when to use this tool over alternatives. The description only implies validation, but does not state preconditions, exclusions, or mention alternative tools for scoring or reading.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
- First observed
faf_read - First observed
faf_score - First observed
faf_validate
Frequently Asked Questions
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, then choose Claim with GitHub. An organization namespace such asio.github.acme/serveralso 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.jsonon 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.
Discussions
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Glama MCP Gateway
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TDQS
Each tool has a clearly distinct purpose: reading/normalizing, scoring completeness, and validating with errors/warnings. Even though faf_score and faf_validate both return a score, the validation adds diagnostic information, reducing overlap.
All three tools follow a consistent faf_verb pattern using snake_case, with verbs 'read', 'score', and 'validate'. No mixing of conventions or unclear names.
Three tools is a well-scoped set for a specialized server handling .faf files. It covers essential operations without being bloated or insufficient for the niche domain.
The server covers reading, scoring, and validating .faf files, which are key analysis operations. However, it lacks tools for creating, writing, or modifying .faf files, which may be a gap depending on use case.