Convert documents and web pages to clean Markdown: PDF, DOCX, XLSX, EPUB, scanned files, any URL.
Convert base64-encoded file contents to Markdown.
filename is required and drives format detection (e.g. "report.docx").
Intended for programmatic MCP clients; for anything with a URL, prefer
convert_url.
| Name | Required | Description | Default |
|---|---|---|---|
| filename | Yes | ||
| content_base64 | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the behavioral burden. It does disclose that the output is Markdown and that filename drives format detection, which is useful. However, it does not mention constraints like size limits, invalid base64 handling, or whether any external call is involved. The description is minimally acceptable but leaves 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 concise: three short sentences with no filler. The main purpose is front-loaded, followed by essential parameter context and a routing note. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple conversion tool, the description covers the main purpose, key parameter, and an alternative sibling. However, it omits guidance about which formats are supported, despite the existence of list_supported_formats as a sibling. It also does not mention error cases or output structure beyond 'Markdown'. Adequate but with noticeable gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does explain filename semantics with an example ('report.docx') and notes that it drives format detection. content_base64 is only implied by the tool name and overall description, and there is no detail on the expected base64 format (e.g., raw vs data URI). This is partial compensation, not complete.
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: converting base64-encoded file contents into Markdown. It also differentiates itself from the sibling tool convert_url by explicitly noting that URLs should use the other tool. This gives an agent a precise, non-ambiguous understanding of what this tool does.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: it is intended for programmatic MCP clients, and it explicitly says to prefer convert_url for anything with a URL. It also highlights that filename is required and drives format detection, which is essential for correct usage. This is strong routing information.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Fetch a URL (web page, PDF, Office doc, and more) and convert it to Markdown.
Works on publicly accessible URLs. Web pages are converted from their
served HTML; JavaScript-rendered content may be incomplete. Optional
filename_hint (e.g. "report.pdf") helps format detection when the URL
has no file extension.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| filename_hint | No |
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 discloses the conversion behavior, the limitation on public URLs, and the JS-rendering caveat, plus how filename_hint influences format detection. It does not cover failure modes or output format details, but for a read-oriented conversion tool this is a reasonable level of 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 concise, front-loaded with the main purpose, and each sentence adds meaningful information without redundancy. It covers scope, constraints, a behavioral caveat, and parameter guidance in a compact structure.
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 only 2 parameters and no output schema, the description covers the essential selection criteria and usage constraints. It does not specify the exact return format beyond 'Markdown' or discuss error handling, but the sibling list_supported_formats likely covers format details, so the description is adequate for an agent to invoke it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameter descriptions (0% coverage), so the description must compensate. It explains the purpose of filename_hint with a concrete example and clarifies the URL must be publicly accessible. The url parameter is less explicitly detailed, but the context makes its semantics clear.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Fetch') and resource ('URL') and states the conversion output ('Markdown'). It also lists supported input types (web page, PDF, Office doc) and distinguishes itself from siblings like convert_base64 by focusing on fetching URLs rather than converting base64 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?
It provides clear context for when to use the tool: requires publicly accessible URLs and warns that JavaScript-rendered content may be incomplete. However, it does not explicitly name alternatives or state 'use this instead of convert_base64', though the URL-focused scope makes the intended use obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List supported input formats, per-tier limits, and which features need Pro.
Single source of truth — do not claim capabilities beyond what this returns.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the behavioral burden. It discloses that the tool is authoritative and exhaustive ('Single source of truth') and warns against extrapolating beyond its output. The verb 'List' also implies a read-only operation, though it does not explicitly confirm absence of side effects.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences with no filler. The first sentence states what the tool returns; the second adds an important authority constraint. Every word contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers what the response contains (formats, tier limits, Pro features) and its authoritative nature. With no output schema, it could go slightly further in describing the response shape or grouping, but for a zero-parameter reference tool this is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so there is nothing for the description to add about parameters. Per the calibration rule, this earns the zero-parameter baseline of 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('List') and a clear resource ('supported input formats'), and further specifies the content categories: per-tier limits and Pro feature requirements. This distinguishes it from sibling conversion and usage tools without requiring schema inspection.
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 'Single source of truth — do not claim capabilities beyond what this returns' sentence provides clear context: use this tool as the authoritative reference before asserting supported capabilities. It does not explicitly name sibling tools or spell out when not to use it, so it falls just short of full exclusionary guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Check your tier and remaining conversions before burning a call.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 does convey that this is a read-only status check and implies the check itself does not consume a conversion, but it does not explicitly state side effects, whether the call is free, or how it behaves when no conversions remain. Adequate but not fully transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence, very concise, with the core action ('check tier and remaining conversions') front-loaded and the usage context appended. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, no-output-schema tool, the description covers what the tool reports (tier and remaining conversions) and when to call it. It does not specify output structure or numeric formats, but that is likely unnecessary for invoking the tool successfully.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty, so there are no parameter details to add. The description correctly focuses on behavior rather than redundant parameter information.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('check') and resource ('your tier and remaining conversions'), and the phrase 'before burning a call' clarifies its role as a preflight status check. It is clearly distinct from the sibling conversion tools, which perform different operations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly indicates when to use the tool: before consuming a call/conversion. It does not name alternatives or exclusion conditions, but the sibling tools are unrelated enough that no further routing is needed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
convert_base64convert_urllist_supported_formatsusage_statusClaiming proves that you control a remote MCP connector. It does not move, proxy, or interrupt the server.
Open the connector listing, choose Claim ownership, and sign in to Glama.
Complete one verification method:
GitHub identity — fastest for official registry listings. For a namespace such as io.github.alice/server, link the matching GitHub user, then choose Claim with GitHub. An organization namespace such as io.github.acme/server also needs that organization to have installed the Glama AI GitHub App and approved its permissions, because GitHub discloses organization membership only to apps it has installed. Use HTTP or DNS when it has not.
HTTP challenge — works when you can deploy a public file. Generate a token, publish the exact JSON Glama shows at /.well-known/glama.json on the same origin as the connector, then choose Check HTTP challenge.
DNS challenge — works when you control DNS but cannot change the server. Generate a token, create the exact TXT record Glama shows, wait for it to propagate, then choose Check DNS challenge.
After verification, Glama sends a confirmation email and gives you access to listing details, thumbnails, health checks, and analytics. Keep the HTTP file or DNS record in place: Glama periodically checks it and ownership remains verified while the token is discoverable.
The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
}Claim tokens are opaque, stable, and bound to the signed-in Glama account. They contain no email address or other personal information. If Glama can no longer discover a verified HTTP or DNS token, it starts a seven-day grace period before removing claim-based access. Restore the same token during that period to keep ownership verified. Never publish an email address, Glama session token, GitHub token, or connector credential as ownership proof.
If verification fails, confirm that you copied the current token exactly. The HTTP file must be public, return valid JSON with a successful HTTP response, and stay on the connector's origin. DNS changes may need more time to propagate. A claim cannot transfer to a different origin or hostname: if the connector target changes, Glama starts the grace period and the new target must be claimed separately after the previous claim is released.
For a connector linked to the official MCP Registry, registry updates continue to replace its name, description, and URL by default. After claiming, open Manage connector and enable Use Glama listing details as the source of truth if edits made on Glama should be preserved. Categories and thumbnails are always managed on Glama; registry linkage and technical connection settings continue to sync.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
To improve your MCP server's ranking:
Claim ownership of the server listing
Complete the server profile with an accurate description and thumbnail
Provide a test profile so Glama can connect to and evaluate the server
Keep tool definitions clear and complete to earn a high Tool Definition Quality Score (TDQS)
Route real usage through the Glama Gateway; more recorded successful server uses also improve the ranking
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
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Convert PDF, DOCX, HTML, and URLs to clean, LLM-ready markdown with tables preserved
High-fidelity PDF to structured Markdown conversion and document field extraction.
Parse PDF/Word/PPT/HTML to Markdown; tables as JSON, image extraction, RAG chunking, page ranges.
Turn any URL into clean Markdown and structured data. Scrape, crawl, search and extract.
Add one secure layer between your agents and this server.
Each tool has a distinct role: the two conversion paths (base64 vs URL) are clearly differentiated by input source and usage guidance, and the two informational tools (format support, usage quota) do not overlap.
convert_base64, convert_url, and list_supported_formats follow a consistent verb_noun pattern. usage_status is a minor resource-style exception but remains readable and does not create real confusion.
Four tools are well-scoped for this service: two conversion entry points plus two supporting tools for format discovery and usage awareness. No redundant or filler tools are present.
The surface covers the full workflow for a stateless conversion service: ingest via base64 or URL, discover supported formats and limits, and check quota before conversion. There are no obvious dead ends or missing core operations.