Fabric MCP Server

by mpzarde

Overview Schema Related Servers Score Discussions

JavaScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v1.0.1
Disambiguation4/5
Each tool has a clear primary purpose, but run_fabric_pattern overlaps with the specific fabric_* tools, and analyze_youtube_video duplicates the combination of get_youtube_transcript and a pattern. These overlaps are mostly resolved by explicit guidance, leaving minor ambiguity.
Naming Consistency3/5
Naming is mixed: some tools use the fabric_ prefix (fabric_create_coding_project, fabric_summarize) while others do not (health_check, analyze_file). Verbs are clear and readable, but the lack of a uniform prefix or verb-object structure across all tools makes the set less predictable.
Tool Count3/5
With 19 tools, the server is slightly heavy. Many specific fabric_* pattern wrappers could be handled by the generic run_fabric_pattern, making the count feel padded. However, the wide range of content analysis tasks justifies the number to some extent.
Completeness5/5
The generic run_fabric_pattern ensures any Fabric pattern can be executed, covering missing specific patterns. Health check and list_fabric_patterns support the operational lifecycle, and content ingestion via transcript, file, and URL is comprehensive. No obvious dead ends exist.
Average 3.3/5 across 19 of 19 tools scored. Lowest: 2/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on February 15, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description gives no behavioral details such as output format, processing steps, or limitations. With no annotations, it fails to disclose any safety or side-effect information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
At one sentence, it is concise but under-specified. It delivers a minimal statement without earning its place through useful details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool description is incomplete for a tool that presumably performs complex analysis. Without output schema or behavioral notes, it doesn't convey what results to expect or how the analysis works.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes the 'input' parameter as 'The input text to process' (100% coverage). The tool description adds no additional parameter guidance, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description names the resource type (academic papers/technical documents) but uses the generic verb 'analyze' without specifying the nature of the analysis. It doesn't clearly distinguish from sibling tools like fabric_analyze_claims or fabric_summarize, since 'analyze' could overlap with those.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines1/5
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 versus the many sibling analysis tools. The description lacks context about use cases, prerequisites, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It only says 'analyze' without specifying return format, side effects, permissions, or what constitutes 'issues and patterns.' It also fails to clarify whether input is raw log text or a file reference, a significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with no fluff or extraneous text. However, it is too sparse to be genuinely useful; a single sentence omits critical details, so it is minimally adequate rather than well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one parameter and no output schema, the description should clarify acceptable input format and what output to expect. It does neither, leaving significant gaps for correct invocation. The low complexity does not excuse the lack of essential details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single parameter, but the description's reference to 'log files' contradicts the schema's 'input text to process,' adding confusion rather than clarity. Since the description does not enrich the parameter meaning and introduces a mismatch, the score is below the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function—analyze log content for issues and patterns—which differentiates it from sibling analysis tools. However, the phrase 'log files' conflicts with the input schema's description of 'input text,' creating ambiguity about whether it accepts file paths or raw content.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
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 versus alternatives like fabric_analyze_incident or fabric_analyze_claims. There are no explicit when-to-use or exclusion statements, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full responsibility for behavioral disclosure. It only states the core action ('Explain code') without revealing any behavioral traits such as input format expectations, output structure, language support, or whether the operation is read-only or potentially time-consuming. This is a significant gap for an agent deciding whether to invoke it.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of one short, front-loaded sentence that directly states the tool's purpose without any filler. Every word earns its place, making it highly concise and structurally clean.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a simple tool with one parameter, but the description is incomplete for an agent. It lacks any indication of the return value or output format, and without an output schema or annotations, the agent cannot anticipate the result of calling the tool. The description also offers no usage context or constraints.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides 100% coverage of the single parameter, describing it as 'The input text to process'. The description adds no additional semantic meaning beyond this, so the baseline score of 3 is appropriate per the rubric.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the specific verb 'explain' with a clear resource ('code') and adds the qualifier 'in simple terms', which provides some scope. However, it does not differentiate from sibling tools like fabric_summarize or fabric_improve_writing, lacking any mention of the specific output style or context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. It does not mention any prerequisites, exclusions, or comparisons to sibling tools, leaving the agent to infer usage from the name and description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior1/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for disclosing behavioral traits. It only says 'analyze' without explaining what happens to the input, what the output looks like, whether there are side effects, or any other behavioral details. This is a significant gap for a tool with no annotation safety hints.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that front-loads the action and subject. Every word earns its place, with no redundancy or filler. It is appropriately sized for the simplicity of the tool.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there is no output schema and no annotations, the description should explain the return value or behavior, but it does not. The tool is simple, but the description leaves critical gaps about expected output and usage context, making it incomplete for an agent to confidently invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already provides a 100% description of the 'input' parameter as 'The input text to process'. The tool description adds meaningful context by specifying that the input should relate to security incidents or system failures, helping the agent understand the expected content. This goes beyond the generic schema description, so it earns above the baseline.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Analyze') and a specific resource ('security incidents or system failures'). It is not a tautology and gives a good sense of the tool's purpose. However, it does not explicitly distinguish itself from sibling tools like fabric_analyze_logs or fabric_analyze_claims, which also perform analysis on different subjects.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus the many sibling analysis tools. It lacks context about appropriate scenarios, prerequisites, or alternatives, making it hard for an agent to know when to select this tool over others.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It only says 'improve writing quality and clarity' without explaining how the tool processes input, whether it returns the improved text, or if it requires any additional context. This leaves the agent guessing about the tool's behavior and output.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clearly worded sentence with no redundancy. It is concise and front-loaded with the primary action. However, it could be expanded with usage guidance without becoming overly verbose, so it misses a perfect score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple (one parameter, no output schema), but the description is too terse to be fully complete. It fails to mention what the tool returns (e.g., the improved text) or when to use it relative to siblings. Given the lack of annotations and output schema, the description should provide more context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers the sole parameter 'input' with description 'The input text to process'. The tool description adds the context that this input is writing to be improved, which provides slight extra meaning. Since schema coverage is 100%, the baseline of 3 is appropriate; no further parameter detail is needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Improve writing quality and clarity' clearly states the tool's function with a specific verb ('improve') and resource ('writing'). It distinguishes from sibling tools that analyze, summarize, or extract content. However, it lacks specificity about the type of improvement (grammar, style, etc.), keeping it slightly generic.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description offers no guidance on when to use this tool versus alternatives. It does not mention scenarios like 'use this to polish a draft' or exclude cases like summarizing. There is no comparison to sibling tools, leaving the agent without explicit usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description only states the basic conversion function and does not disclose any additional behavioral traits such as input constraints, output format, network usage, or permissions. With no annotations, the description carries the full transparency burden, which it does not fulfill.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, succinct sentence with no redundant information. It is appropriately sized for the tool's simplicity and front-loads the key action.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Although the tool has a simple interface, the description lacks context about acceptable input types, output format, or usage scenarios. Given the absence of annotations and output schema, the description is insufficiently complete for an agent to fully understand the tool's behavior.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema already documents the 'input' parameter fully with 100% coverage, describing it as 'The input text to process'. The description does not add any extra meaning beyond calling the input 'content', so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool converts content into flashcards, which is a specific verb and resource. It does not explicitly differentiate from sibling tools like fabric_create_quiz, but the core purpose is unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as fabric_create_quiz or fabric_summarize. No context, prerequisites, or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It does not disclose what the generated 'project structure and planning' looks like, whether it modifies the filesystem, returns a plan document, or any limitations. The one-liner is too vague to convey behavioral expectations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, clear sentence with no redundant words. It is front-loaded with the key action and resource. However, it is slightly under-specified, which prevents a perfect conciseness score.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema, no annotations, and only a vague one-sentence description, the tool is not adequately contextualized. The agent cannot anticipate what the output or behavior will be, even for a simple one-parameter tool. More detail is needed about the generated artifact and any side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with the only parameter 'input' described as 'The input text to process'. The description adds 'from an idea' which slightly clarifies the input semantics, but does not provide additional syntax or format details. Baseline of 3 is appropriate as the schema already documents the parameter.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('generate') with a clear resource ('project structure and planning') and source ('from an idea'). It clearly differentiates this tool from sibling tools like fabric_explain_code and fabric_create_quiz, which have distinct purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, no exclusions, and no mention of prerequisites. It merely states what the tool does without contextual usage signals.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry full transparency. It only states the core action without disclosing output format, error behavior, authentication needs, or rate limits. The description adds minimal behavioral context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that clearly states the action and resource. It has zero wasted words and is appropriately concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has a single required parameter and no output schema, so the description is nearly sufficient. However, it lacks mention of the return format, language limitations, or edge cases (e.g., missing transcript), making it minimally viable but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully describes the only parameter 'url' with 100% coverage. The description adds no meaningful semantics beyond what the schema already provides, so the baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Fetch') and resource ('transcript from a YouTube video URL'). However, it does not differentiate from sibling tools like analyze_youtube_video, so it lacks explicit sibling differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives such as analyze_youtube_video or other fabric tools. It does not mention prerequisites, use cases, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full responsibility for behavioral disclosure, but it only states the operation without revealing the output format, input limitations, or any side effects. It does not even mention that it returns a summary text.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is a single, front-loaded sentence with no filler, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has no output schema and no annotations, so the description should compensate by explaining return values and constraints. It offers only a bare statement, leaving the user to guess what kind of summary will be produced and how it will be returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema fully documents the single 'input' parameter with a description, and the tool description adds no additional semantic detail beyond the word 'content'. Baseline 3 applies due to high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses an explicit verb ('Create') and resource ('summary of content'), clearly defining the tool's function. It distinguishes from siblings like fabric_explain_code or fabric_analyze_claims by focusing on summarization.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to choose this tool over alternatives such as fabric_extract_wisdom or fabric_summarize_git_diff. The description only states what it does, not when to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It does not mention whether the operation is read-only, what output format to expect, any side effects, or any potential limitations. The description only states the high-level action without exposing behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, direct sentence that communicates the core action. There is no redundant or extraneous content, making it appropriately sized and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one input parameter, but there is no output schema and the description does not explain what the result looks like. Given the lack of annotations and output schema, the description leaves a noticeable gap regarding return values. However, because the complexity is low, the description is minimally acceptable but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has one parameter with a description ('The input text to process'), giving 100% schema_description_coverage. The tool description adds no additional parameter semantics beyond what the schema already provides, so the baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('analyze and fact-check') and a specific resource ('claims made in content'). This distinguishes it from sibling tools like fabric_analyze_logs or fabric_analyze_paper, which target different content types.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or cases where another sibling tool would be more appropriate. This is essentially a bare functional statement.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It only states the general action and does not reveal important traits such as the output format (e.g., multiple-choice questions), whether it invokes an external pattern, any limitations on input size, or how the questions are generated. This lack of transparency could leave an agent uncertain about side effects and expected results.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that immediately states the tool's function. It is front-loaded with the key action and object, and contains no filler or redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is relatively simple with one parameter and no output schema, so the description should clarify what the generated quiz questions look like or any usage prerequisites. It does not mention output format, question types, or additional settings, leaving gaps in context for an agent. However, the basic purpose is clear enough for basic selection, hence a middle score.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% as the only parameter 'input' is described as 'The input text to process'. The description adds minor semantic alignment by referring to 'content', but it does not elaborate on required structure, formatting, or examples beyond the schema. Since the schema already provides adequate meaning, a baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb and resource: 'Generate quiz questions from content for learning'. It distinguishes itself from sibling tools like fabric_to_flashcards by focusing on quiz questions rather than flashcards, and from other content-processing tools by the explicit output type.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to use this tool versus alternatives. The description merely states what it does, but never mentions scenarios, exclusions, or comparisons to similar tools like fabric_to_flashcards or fabric_analyze_claims. Without explicit usage context, an agent may misuse it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears the full burden of behavioral disclosure. It only says 'run' without stating whether the operation is read-only, what the output format is, if there are side effects, permission needs, or failure modes. This is a significant gap for a generic execution tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and every word is necessary. It avoids fluff and gets directly to the point, making it highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a generic tool with no output schema and no annotations, the description is incomplete. It does not explain what 'run' returns, how to interpret results, or any operational context (e.g., errors, timeouts). Given the tool's role as a fallback, more guidance on expected behavior is needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters (pattern and input) with clear descriptions, so schema coverage is 100%. The tool description adds little beyond the schema, only mentioning 'custom input', which is vague and does not enhance semantic understanding. Baseline 3 is appropriate when schema effectively documents parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs any Fabric pattern with custom input, using the specific verb 'run' and resource 'Fabric pattern'. It also distinguishes itself from sibling tools by noting it is for patterns not covered by specific tools, making its purpose explicit and differentiated.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Use this for patterns not covered by specific tools' provides clear contextual guidance on when to choose this tool over the sibling-specific ones. It implies an exclusion (don't use if a specific tool exists), though it could be more explicit about the 'when not to use' scenario.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states the action and purpose, without mentioning side effects, permissions, limitations, or how the summary is produced. This leaves the agent without critical safety or operational context.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, compact sentence with no redundant information. It is front-loaded with the action and resource, making it easy to scan.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is simple with one parameter, but the description provides no output schema and minimal behavioral detail. Given the many overlapping sibling tools (e.g., fabric_summarize, fabric_explain_code), the lack of usage exclusions leaves the description only partially complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema's parameter description is generic ('The input text to process'), but the tool description clarifies that the input should be git diff output. This adds meaningful semantic context beyond the schema, which is especially valuable for a single-parameter tool.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Summarize git diff output for code reviews' uses a specific verb ('summarize') with a clear resource ('git diff output') and an explicit purpose ('for code reviews'). This clearly distinguishes it from generic summarization tools like fabric_summarize and code-specific tools like fabric_explain_code.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'for code reviews' implies the tool is intended for code-review contexts, giving some usage guidance. However, it does not explicitly contrast with sibling tools such as fabric_summarize or fabric_explain_code, nor does it state when not to use it. The guidance is implied rather than explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It adds the behavioral trait that complete content is processed without truncation. However, it does not disclose potential network failures, content type limitations, rate limits, or side effects of fetching external URLs, which would be valuable for this type of tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the core action, and every sentence adds necessary context. The second sentence reinforces the benefit (no truncation) without redundancy, making it concise and well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and no annotations, the description is the sole source of context. It covers the main purpose and a key benefit, but lacks details on return format, error handling, URL constraints, and other edge cases that would be important for an agent to invoke this tool correctly and anticipate outcomes.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with both parameters described. The description adds minimal semantic value beyond the schema, only echoing that a Fabric pattern is applied and mentioning the URL as a source. The schema already provides the necessary parameter explanations.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches content from a URL and analyzes it with a Fabric pattern in one step, using a specific verb and resource. It distinguishes from siblings by combining fetch+analyze on URLs, unlike analyze_file or run_fabric_pattern, and mentions content types (articles, documentation, blog posts).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on when to use the tool: for URL-based content like articles, documentation, and blog posts. It does not explicitly exclude alternatives or name siblings, but the content scope is clear enough to differentiate from video or local file tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 does add value by stating that the complete file content is processed without truncation, a useful behavioral guarantee. However, it omits details like permissions, error handling, or return format, leaving some transparency gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise at three sentences, each serving a distinct purpose: action, key behavioral detail, and use cases. There is no fluff or repetition, making it well-structured and easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given two well-documented parameters and no output schema, the description covers the core purpose, the main behavioral nuance (no truncation), and typical applications. It does not explain return values or prerequisites, but these are not critical for a straightforward read-and-analyze tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so both file_path and pattern are already documented with descriptions. The description does not add new parameter semantics beyond what the schema provides, hence the baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads a file and applies a Fabric pattern in one step, using specific verbs ('read', 'analyze') and resources ('file', 'pattern'). It distinguishes itself from sibling tools by emphasizing the combined operation and the no-truncation guarantee, making its unique role clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage context: 'Useful for analyzing documents, code files, logs, etc.' This indicates appropriate scenarios without discussing exclusions or alternative tools. While not as explicit as naming alternatives, the context is clear enough to guide selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It implies a read-only operation and suggests the output (insights, quotes, wisdom), but it does not clarify that the input must be text, which could mislead about handling videos/podcasts directly. This creates slight ambiguity but not a contradiction.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence that efficiently conveys purpose and scope without unnecessary words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with one parameter and no output schema, the description is fairly complete: it states what it does, the types of content, and what output to expect. It misses the explicit clarification that videos/podcasts require transcribed text as input, which is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already provides full coverage for the single parameter ('The input text to process'). The description adds context about accepted content types but no additional syntax or format details beyond the schema, resulting in baseline 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'Extract' with a clear resource ('key insights, quotes, and wisdom') and scope ('articles, videos, podcasts'). It distinguishes itself from sibling tools like fabric_summarize by focusing on extracting wisdom rather than summarizing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context on what content this tool handles ('articles, videos, podcasts'), but it does not explicitly mention when not to use it or name alternative tools. There is enough context to infer usage, but no explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that installation instructions may be provided, adding behavioral context. However, it does not describe the output format, whether it is read-only, or what happens if dependencies are missing beyond providing instructions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence that efficiently conveys the tool's action and a key behavior (providing installation instructions). There is no redundant or filler content.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, zero-parameter tool, the description is adequate: it states what is checked and that instructions may be provided. Since there is no output schema, it could have been more explicit about the exact return format, but the core context is covered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the baseline is 4. The description correctly avoids parameter explanations, and there is no schema to compensate for, making this dimension unproblematic.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with a specific verb ('check') and resource ('Fabric, yt-dlp, and other dependencies'). It also distinguishes itself from sibling tools, which are all content/analysis tools, by focusing on environment setup verification.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context (checking environment readiness) but does not explicitly mention when to use it over alternatives or exclude scenarios. No alternative tools are named, though the sibling list makes the diagnostic role clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It indicates a read-only list operation but does not disclose return format or whether the list is ordered or paginated. This is acceptable for a simple listing tool but leaves some behavioral details unstated.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no unnecessary words. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and no output schema, the description fully explains the tool's purpose. The context is complete for a list-all operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the description of the action is sufficient. Baseline 4 is appropriate since no parameter details are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb 'list' and identifies the resource 'all available Fabric patterns'. It clearly distinguishes from siblings like run_fabric_pattern, which executes a pattern, and other pattern-specific tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The usage is implied by the description: use this tool to discover available patterns before running them. However, it does not explicitly mention alternatives or exclusions, which is less critical for a simple listing tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses one key behavior (complete transcript processed without truncation) but does not mention other relevant aspects such as authentication, rate limits, error handling, or output format. This is minimal but not misleading.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and front-loaded, with two sentences that both serve a purpose: stating the action and providing usage guidance. There is no redundant information or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (2 parameters, no output schema, no annotations), the description covers the essential purpose and usage. It adequately addresses the main sibling tool (get_youtube_transcript) and explains the key benefit, but it could optionally mention what the result looks like or any prerequisites.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for both parameters (url and pattern), with clear descriptions in the schema. The description does not add extra parameter semantics beyond what the schema already provides, so a baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool fetches a YouTube transcript and analyzes it with a Fabric pattern in one step. It distinguishes itself from sibling tools by explicitly saying to use this instead of calling get_youtube_transcript and then a pattern separately.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit usage guidance by recommending this tool over the alternative of calling get_youtube_transcript and then a pattern separately. It also gives a rationale (ensures complete transcript without truncation), which clarifies when this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![fabric-mcp MCP server](https://glama.ai/mcp/servers/mpzarde/fabric-mcp/badges/card.svg)](https://glama.ai/mcp/servers/mpzarde/fabric-mcp)

Score Badge

Copy to your README.md:

[![fabric-mcp MCP server](https://glama.ai/mcp/servers/mpzarde/fabric-mcp/badges/score.svg)](https://glama.ai/mcp/servers/mpzarde/fabric-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/mpzarde/fabric-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server