Markdownify MCP Server
Markdownify MCP-Server

Markdownify ist ein Model Context Protocol (MCP)-Server, der verschiedene Dateitypen und Webinhalte in das Markdown-Format konvertiert. Er bietet eine Reihe von Tools, um PDFs, Bilder, Audiodateien, Webseiten und mehr in leicht lesbaren und teilbaren Markdown-Text umzuwandeln.
Funktionen
Konvertierung verschiedener Dateitypen in Markdown:
PDF
Bilder
Audio (mit Transkription)
DOCX
XLSX
PPTX
Konvertierung von Webinhalten in Markdown:
YouTube-Videotranskripte
Bing-Suchergebnisse
Allgemeine Webseiten
Abrufen vorhandener Markdown-Dateien
Related MCP server: Markdownify MCP Server
Erste Schritte
Repository klonen
Abhängigkeiten installieren:
bun installDer
preinstall-Schritt erstellt eine Python-virtuelle Umgebung unter.venvund installiertmarkitdown[all].Projekt bauen:
bun run buildServer starten:
bun start
Entwicklung
Verwende
bun run dev, um den TypeScript-Compiler im Watch-Modus zu startenÄndere
src/server.ts, um das Serververhalten anzupassenFüge Tools in
src/tools.tshinzu oder ändere sie
Verwendung mit Desktop-App
Um diesen Server in eine Desktop-App zu integrieren, füge Folgendes zur Serverkonfiguration deiner App hinzu:
{
"mcpServers": {
"markdownify": {
"command": "node",
"args": [
"{ABSOLUTE PATH TO FILE HERE}/dist/index.js"
]
}
}
}Umgebungsvariablen
Alle Pfade sind standardmäßig auf sinnvolle Werte eingestellt; überschreibe sie nur, wenn die Standardwerte nicht zu deinem Installationslayout passen.
Variable | Standardwert | Zweck |
|
| Absoluter Pfad zur |
|
| Absoluter Pfad zur |
| nicht gesetzt (uneingeschränkt) | Durch Pfadtrennzeichen getrennte Liste ( |
| nicht gesetzt | Veralteter Alias für |
Verwendung mit Docker
Bauen und Ausführen:
docker build -t markdownify-mcp .
docker run --rm -i \
-v "$HOME/Documents:/data:ro" \
-e MD_ALLOWED_PATHS=/data \
markdownify-mcpHinweise für den Docker MCP-Katalog (mcp/markdownify):
Hänge alle Host-Verzeichnisse, die der Server lesen soll, in den Container ein und übergebe dann die Container-Pfade an die Tools (z. B.
/data/foo.pdf, nicht/Users/you/Documents/foo.pdf).Setze
MD_ALLOWED_PATHSauf die durch Doppelpunkte getrennte Liste der eingehängten Verzeichnisse, damit der Server eine Lesebegrenzung erzwingt, die dem Bind-Mount entspricht.Das veröffentlichte Docker-Image installiert nur
markitdown[pdf]— Audiotranskription und Bild-OCR (audio-to-markdown,image-to-markdown) erfordern die[all]-Extras und schlagen im Slim-Image fehl. Verwende die lokale Installation (bun install) für den vollen Funktionsumfang.
Verfügbare Tools
youtube-to-markdown: Konvertiert YouTube-Videos in Markdownpdf-to-markdown: Konvertiert PDF-Dateien in Markdownbing-search-to-markdown: Konvertiert Bing-Suchergebnisse in Markdownwebpage-to-markdown: Konvertiert Webseiten in Markdownimage-to-markdown: Konvertiert Bilder in Markdown mit Metadatenaudio-to-markdown: Konvertiert Audiodateien in Markdown mit Transkriptiondocx-to-markdown: Konvertiert DOCX-Dateien in Markdownxlsx-to-markdown: Konvertiert XLSX-Dateien in Markdownpptx-to-markdown: Konvertiert PPTX-Dateien in Markdownget-markdown-file: Ruft eine vorhandene Markdown-Datei ab. Die Dateiendung muss auf*.mdoder*.markdownenden.OPTIONAL: Setze
MD_ALLOWED_PATHS, um jedes Dateieingabe-Tool auf eine Liste von Verzeichnissen zu beschränken, z. B.MD_ALLOWED_PATHS=/data/in:/data/out bun start.
Mitwirken
Beiträge sind willkommen! Bitte reiche gerne einen Pull Request ein.
Lizenz
Dieses Projekt ist unter der MIT-Lizenz lizenziert - siehe die LICENSE-Datei für Details.
Available Tools
10 toolsaudio-to-markdownB
Convert an audio file to markdown, including transcription if possible
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path of the audio file to convert |
TDQS
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 mentions 'including transcription if possible', which hints at potential limitations, but doesn't specify what happens when transcription fails, required audio formats, processing time, or output structure. For a tool with no annotations, this leaves significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose. It's front-loaded with the core functionality and includes a useful qualification ('if possible'). Every word earns its place with zero waste.
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 moderate complexity (audio processing and transcription), no annotations, no output schema, and 100% schema coverage, the description is minimally adequate. It covers the basic purpose but lacks details about behavioral constraints, output format, or error conditions that would be helpful for an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% description coverage, with the 'filepath' parameter clearly documented. The description adds no additional parameter information beyond what the schema provides. According to the rules, when schema coverage is high (>80%), the baseline score is 3 even with no param info in the 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's purpose: 'Convert an audio file to markdown, including transcription if possible'. This specifies the verb ('convert'), resource ('audio file'), and output format ('markdown'), distinguishing it from siblings that convert other file types. However, it doesn't explicitly differentiate from all siblings (e.g., 'youtube-to-markdown' also involves audio conversion).
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools, prerequisites, or specific contexts where audio-to-markdown is preferred over other conversion tools. The phrase 'if possible' hints at limitations but doesn't specify conditions for successful transcription.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
bing-search-to-markdownC
Convert a Bing search results page to markdown
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the Bing search results page |
TDQS
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 states the conversion action but lacks details on permissions, rate limits, error handling, or output format (e.g., markdown structure). For a tool with no annotation coverage, this leaves significant gaps in understanding its behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's front-loaded with the core action ('Convert'), making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete. It doesn't explain what the markdown output looks like, potential limitations (e.g., handling dynamic content), or how it differs from similar tools. For a conversion tool with no structured behavioral data, more context is needed.
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%, with the single parameter 'url' clearly documented in the schema. The description doesn't add any meaningful semantics beyond what the schema provides (e.g., it doesn't specify URL format constraints or examples), so it meets the baseline for high schema coverage without compensating value.
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's purpose with a specific verb ('Convert') and resource ('Bing search results page'), making it easy to understand what it does. However, it doesn't explicitly differentiate from sibling tools like 'webpage-to-markdown' or 'youtube-to-markdown', which might handle similar conversion tasks for 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.
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 doesn't mention prerequisites (e.g., needing a valid Bing search URL), exclusions (e.g., not working with non-Bing pages), or comparisons to sibling tools like 'webpage-to-markdown' that might handle generic web pages.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
docx-to-markdownC
Convert a DOCX file to markdown
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path of the DOCX file to convert |
TDQS
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 states the conversion action but lacks behavioral details such as whether it overwrites files, requires specific permissions, handles errors, or produces output location/format. For a file conversion tool with zero annotation coverage, this is a significant gap in transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is front-loaded with the core action and resource, making it highly concise and well-structured for quick understanding.
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 complexity of file conversion, lack of annotations, and no output schema, the description is incomplete. It does not address behavioral aspects like output handling, error conditions, or dependencies, leaving gaps for an AI agent to correctly invoke the tool in context with siblings.
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%, with the single parameter 'filepath' fully documented in the schema as 'Absolute path of the DOCX file to convert'. The description does not add any meaning beyond this, as it only implies the parameter indirectly. Baseline 3 is appropriate when the schema does the heavy lifting.
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 'Convert a DOCX file to markdown' clearly states the action (convert) and resource (DOCX file) with the target format (markdown). It distinguishes from most siblings by specifying DOCX format, though it doesn't explicitly differentiate from other document converters like pptx-to-markdown or pdf-to-markdown beyond the file type mention.
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 no guidance on when to use this tool versus alternatives. With multiple sibling tools for converting different formats to markdown (e.g., pdf-to-markdown, pptx-to-markdown), there is no indication that this is specifically for DOCX files or any prerequisites like file accessibility or format compatibility.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get-markdown-fileC
Get a markdown file by absolute file path
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path to file of markdown'd text |
TDQS
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. While 'Get' implies a read operation, the description doesn't specify what happens if the file doesn't exist, permission requirements, file size limitations, or what format the content is returned in. This leaves significant behavioral questions unanswered.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that communicates the core functionality without any wasted words. It's appropriately front-loaded with the essential information and earns its place with clear communication.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no annotations and no output schema, the description is insufficient. It doesn't explain what happens on success (what format is returned?), what happens on failure (error handling), or how it differs from sibling conversion tools. The context signals indicate this is a simple tool, but the description leaves too many operational questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'filepath' well-documented in the schema as 'Absolute path to file of markdown'd text.' The description adds no additional parameter information beyond what's already in the schema, so the baseline score of 3 is appropriate.
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 action ('Get') and resource ('a markdown file by absolute file path'), making the purpose immediately understandable. However, it doesn't differentiate from sibling tools that convert various formats to markdown, as this tool retrieves existing markdown files rather than converting content.
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 no guidance on when to use this tool versus alternatives. With sibling tools focused on converting various formats to markdown, there's no indication of whether this tool is for retrieving pre-existing markdown files or how it relates to those conversion tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
image-to-markdownB
Convert an image to markdown, including metadata and description
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path of the image file to convert |
TDQS
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 mentions that the conversion includes metadata and description, which adds some context about output behavior. However, it lacks details on error handling, performance characteristics, or any side effects, leaving gaps in understanding how the tool behaves beyond the basic operation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is front-loaded with the core purpose and includes a brief mention of output features, making it easy to understand quickly. Every part of the sentence contributes to clarifying the tool's role.
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 moderate complexity (conversion with metadata) and lack of annotations and output schema, the description is minimally adequate. It covers the basic operation and hints at output content but does not fully address behavioral aspects or provide complete context for effective use. The description meets the minimum viable standard but has clear gaps in usage and transparency.
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 has 100% description coverage, clearly documenting the single parameter 'filepath' as the absolute path of the image file. The description does not add any additional meaning beyond what the schema provides, such as supported image formats or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the schema handles the parameter documentation adequately.
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's purpose: converting an image to markdown, including metadata and description. It specifies the verb 'convert' and the resource 'image', distinguishing it from sibling tools that handle other file types like audio, PDF, or webpage. However, it does not explicitly differentiate from siblings beyond the resource type, as all sibling tools involve conversion to markdown.
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 no guidance on when to use this tool versus alternatives. It does not mention prerequisites, such as the image file needing to be accessible or in a specific format, or when to choose this over other markdown conversion tools for different content types. Usage is implied by the tool name but not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pdf-to-markdownC
Convert a PDF file to markdown
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path of the PDF file to convert |
TDQS
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 states the conversion action but lacks critical details: whether this is a read-only operation, if it modifies the original PDF, what permissions are needed, how errors are handled, or what the output format entails (e.g., markdown structure, quality of conversion). For a tool with no annotation coverage, this is a significant gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with zero waste. It is appropriately sized and front-loaded, directly stating the tool's purpose without unnecessary elaboration. Every word earns its place, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete for a file conversion tool. It doesn't explain what the markdown output looks like (e.g., formatting, metadata), potential limitations (e.g., complex PDFs, images within PDFs), or error conditions. With no structured data to compensate, the description should provide more context about the tool's behavior and results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with the single parameter 'filepath' fully documented in the schema as 'Absolute path of the PDF file to convert'. The description adds no additional parameter semantics beyond what the schema provides, such as file size limits, supported PDF versions, or path format examples. Baseline 3 is appropriate when the schema does the heavy lifting.
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's purpose with a specific verb ('Convert') and resource ('a PDF file to markdown'), making it immediately understandable. It distinguishes from most siblings by specifying PDF input, though it doesn't explicitly differentiate from other file conversion tools like docx-to-markdown or pptx-to-markdown beyond the file type.
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 no guidance on when to use this tool versus alternatives. With multiple sibling tools for converting different file types to markdown (e.g., docx-to-markdown, pptx-to-markdown), there's no indication of when PDF conversion is appropriate or what distinguishes it from other conversion tools. No prerequisites or exclusions are mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
pptx-to-markdownC
Convert a PPTX file to markdown
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path of the PPTX file to convert |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. 'Convert' implies a transformation operation, but it doesn't disclose whether this is read-only or destructive, what permissions are needed, whether it handles large files, what happens on failure, or what the output format looks like. The description adds minimal behavioral context beyond the basic operation.
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 extremely concise at just 5 words: 'Convert a PPTX file to markdown'. Every word earns its place, with no wasted text. It's front-loaded with the core purpose and couldn't be more efficient while still conveying the basic operation.
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 file conversion tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the markdown output contains (slides, text, images?), where the output goes, what formats are supported, or any error conditions. With rich sibling tools and conversion being a non-trivial operation, more context is needed.
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%, with the single parameter 'filepath' well-documented in the schema as 'Absolute path of the PPTX file to convert'. The description doesn't add any parameter semantics beyond what the schema provides, but with complete schema coverage, the baseline score of 3 is appropriate.
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's purpose: 'Convert a PPTX file to markdown' specifies the verb (convert) and resource (PPTX file). It distinguishes from most siblings by specifying PPTX format, though it doesn't explicitly differentiate from other document converters like docx-to-markdown beyond the file type.
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 no guidance on when to use this tool versus alternatives. With multiple sibling conversion tools (docx-to-markdown, pdf-to-markdown, etc.), there's no indication of when PPTX conversion is appropriate versus other formats, nor any prerequisites or constraints mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
webpage-to-markdownC
Convert a webpage to markdown
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the webpage to convert |
TDQS
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. While 'convert' implies a transformation operation, the description doesn't reveal any behavioral traits: it doesn't mention whether this requires internet access, what happens with complex webpages (JavaScript, authentication), error handling, or output format details beyond 'markdown'.
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 extremely concise at just four words, with zero wasted language. It's front-loaded with the core purpose and uses clear, direct phrasing that immediately communicates 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?
For a tool with no annotations and no output schema, the description is insufficiently complete. It doesn't address what the markdown output contains (full content, metadata, links), how it handles webpage elements (tables, images), or any limitations (size, complexity). Given the context of conversion tools with various source types, more guidance is needed.
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 has 100% description coverage, with the single 'url' parameter clearly documented. The description doesn't add any parameter semantics beyond what the schema provides (it doesn't elaborate on URL format requirements, encoding, or validation), so it meets the baseline score when schema coverage is high.
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's purpose with a specific verb ('convert') and resource ('webpage'), making it immediately understandable. However, it doesn't distinguish this tool from its siblings (like 'pdf-to-markdown' or 'youtube-to-markdown'), which all follow the same pattern of converting different source types to markdown.
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 no guidance on when to use this tool versus alternatives. With multiple sibling tools that convert different source types to markdown (audio, PDF, YouTube, etc.), there's no indication that this is specifically for webpages versus other document types, nor any mention of prerequisites or limitations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
xlsx-to-markdownC
Convert an XLSX file to markdown
| Name | Required | Description | Default |
|---|---|---|---|
| filepath | Yes | Absolute path of the XLSX file to convert |
TDQS
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 states the conversion action but lacks critical details: it doesn't mention whether the tool modifies the original file, where the output is saved, what markdown format is used (e.g., for tables), or error handling for invalid files. This leaves significant gaps in understanding the tool's behavior.
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 extremely concise—a single sentence with zero wasted words—and front-loaded with the core action. It efficiently communicates the tool's purpose without unnecessary elaboration, making it easy to parse quickly.
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 lack of annotations and output schema, the description is incomplete for a file conversion tool. It doesn't explain the output (e.g., markdown content or file location), error conditions, or limitations (e.g., file size constraints). While the schema covers the input, the overall context for safe and effective use is insufficient.
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 description adds no parameter-specific information beyond what the input schema provides. With 100% schema description coverage, the schema fully documents the single parameter ('filepath'), so the baseline score of 3 is appropriate. The description doesn't compensate with additional context like file format requirements or examples.
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's purpose with a specific verb ('Convert') and resource ('XLSX file to markdown'), making it immediately understandable. However, it doesn't differentiate from sibling tools like 'docx-to-markdown' or 'pdf-to-markdown' beyond the file format, missing an opportunity to specify what makes XLSX conversion unique (e.g., handling spreadsheets vs. documents).
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 no guidance on when to use this tool versus alternatives. With multiple sibling tools for converting different file types to markdown, it fails to indicate scenarios where XLSX conversion is appropriate (e.g., for tabular data) or when other tools might be better suited, leaving usage context entirely implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
youtube-to-markdownB
Convert a YouTube video to markdown, including transcript if available
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the YouTube video |
TDQS
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 mentions including a transcript 'if available', which hints at conditional behavior, but doesn't explain what happens when a transcript isn't available (e.g., error, fallback, or partial output). It also omits details like rate limits, authentication needs, or output format specifics, leaving gaps for a tool that performs conversion.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the core functionality ('Convert a YouTube video to markdown') and adds a key detail ('including transcript if available'). There is no wasted text, and it's appropriately sized for a tool with one parameter and no complex annotations.
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 moderate complexity (conversion with conditional transcript inclusion), lack of annotations, and no output schema, the description is minimally adequate. It states what the tool does but lacks details on behavioral traits, error handling, or output structure. It meets the basic requirement but leaves significant gaps that could hinder effective use by an AI agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 100% coverage with a single parameter 'url' clearly described. The description doesn't add any parameter-specific information beyond what the schema provides, such as URL format requirements or validation rules. With high schema coverage, the baseline score of 3 is appropriate, as the description doesn't compensate but also doesn't detract.
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's purpose: converting a YouTube video to markdown, including transcript if available. It specifies the verb 'convert' and resource 'YouTube video', making the action clear. However, it doesn't explicitly distinguish this tool from sibling tools like 'audio-to-markdown' or 'webpage-to-markdown', which handle different input types but share the same output format.
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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'audio-to-markdown' (which might handle audio from YouTube) or 'webpage-to-markdown' (which could process YouTube pages), nor does it specify prerequisites such as video accessibility or transcript availability. Usage is implied by the tool 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.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
10 tool updates
- First observed
audio-to-markdown - First observed
bing-search-to-markdown - First observed
docx-to-markdown - First observed
get-markdown-file - First observed
image-to-markdown - First observed
pdf-to-markdown - First observed
pptx-to-markdown - First observed
webpage-to-markdown - First observed
xlsx-to-markdown - First observed
youtube-to-markdown
TDQS
Each tool has a clearly distinct purpose focused on converting a specific input format (audio, Bing search, DOCX, etc.) to markdown. The descriptions specify unique source types, making it impossible to confuse which tool to use for a given conversion task.
All tools follow a consistent pattern of 'source-to-markdown' (e.g., audio-to-markdown, docx-to-markdown). This uniform naming convention makes it easy to predict tool names and understand their functions at a glance.
With 10 tools, the server covers a comprehensive range of common input formats (audio, documents, web content, spreadsheets, presentations, etc.) for markdown conversion. This count is well-scoped for the domain, providing thorough coverage without being overwhelming.
The tool set offers complete coverage for converting various media and file types to markdown, including audio, images, documents, webpages, and videos. There are no obvious gaps; each tool handles a distinct input format, ensuring agents can convert any supported source without dead ends.
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