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Glama

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": true
}
logging
{}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
extensions
{
  "io.modelcontextprotocol/ui": {}
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
parseA

Convert a document to markdown. Routes across backends automatically.

parse_urlC

Shortcut for parsing an HTTP(S) URL. Same return shape as parse.

parse_to_vaultA

Parse a document and write it to the vault as a markdown note.

Replaces the standalone ⚙️ Meta/scripts/markitdown_to_vault.py shell script: same output shape, but with the router's full audit trail (which backend ran, fallback chain, latency) baked into the frontmatter.

interpretA

Parse the document, then ask Claude over the parsed markdown.

Use this for "summarize this PDF", "extract every action item", "what does this contract say about termination" style requests. The router parses first (cheap, deterministic), then Claude reads the markdown and answers. Cache hits across calls in the same session reuse the parsed text for free input tokens.

list_backendsA

Report which parse backends are installed + which are missing.

Returns a list of {name, available, module} entries. Use this to debug "why did the router fall back to markitdown" by checking whether docling/llamaparse are actually available.

benchmarkB

Run every available backend on the same input, compare results.

Diagnostic tool. Returns one ParseResult per available backend with latency, byte counts, error state, and metadata so you can see at a glance which parser handles a given document best.

chunk_textA

Chunk a markdown document into retrieval-ready pieces.

Picks a doc-type-aware chunker (paper / book / manual / qa / resume / table / default) based on document structure. Set doc_type to force a specific chunker; default "auto" runs structural detection.

detect_doc_typeA

Run structural heuristics to detect document type.

Useful when you want to know what chunk_text would pick before actually chunking. Returns the resolved doc_type token.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

TDQS

B3.4/5.0

Scored across 8 tools

Disambiguation3/5

parse, parse_url, parse_to_vault, and interpret all build on the same underlying parse operation, so an agent could easily select the wrong variant when the target or output destination is not explicitly clear. The descriptions help, but the boundaries between these tools remain somewhat fuzzy.

Naming Consistency4/5

Most tools follow a predictable snake_case verb_noun pattern such as list_backends, chunk_text, and detect_doc_type. parse_to_vault and single-word verbs like parse, interpret, and benchmark are minor deviations, but the overall naming style is coherent.

Tool Count5/5

Eight tools is well-scoped for a document parsing server with diagnostics, vault integration, and chunking support. Each tool maps to a distinct workflow need, and none feel redundant or like filler.

Completeness4/5

The toolset covers the full parse-chunk-interpret workflow, plus backend diagnostics and vault output. The main gap is the lack of a batch or multi-document parsing operation, but agents can work around that with repeated calls.

Maintenance

ActivityActive
ResponsivenessUnresponsive