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Read file

read_file
Read-only

Read the content of a file the user uploaded — use this when the answer may live in a document in their Second Brain: schedules, itineraries, contracts, exports, scans. PDFs and images are returned as the actual document, so tables and scanned pages read correctly. Find the file first with search_graph_objects (type 'file') and pass its object_id, or pass part of the filename as name.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoPart of the filename, when the object_id is unknown.
object_idNoThe file's graph object_id (from search_graph_objects).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations only declare readOnlyHint=true, so the description's note that PDFs and images are returned as the actual document with correct table and scan rendering adds meaningful behavioral context. There is no contradiction between the read-only annotation and the described behavior.

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?

Three focused sentences front-load the purpose, then add the use case, special rendering behavior, and lookup guidance. No sentence is redundant or wasted.

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 read-only file-reading tool, the description covers when to use it, how to resolve the file, which parameter to pass, and how PDFs/images behave. It is complete enough to guide correct invocation even without an output schema.

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 the baseline is 3. The description reinforces the object_id-vs-name choice and the search_graph_objects workflow, but it does not materially add parameter semantics beyond what the schema already documents.

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 the content of a user-uploaded file in the Second Brain, with the verb and resource both specific. It distinguishes this from web-page or Obsidian-note readers by narrowing scope to uploaded documents and by naming search_graph_objects as the way to locate them.

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?

It explicitly says to use this when the answer may live in an uploaded document, which gives a clear triggering context. It also provides the lookup workflow with search_graph_objects, but it does not explicitly state when not to use it or name alternative reading tools such as read_web_page.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.8/5.0
Disambiguation4/5

Most tools are scoped to a distinct resource and action, and descriptions do a good job separating close pairs like search_connections vs ask_about_connections or get_my_linkedin_posts vs linkedin_analytics. However, the multiple deletion tools (delete_graph_object, delete_graph_objects, purge_graph_object) and the several file-reading tools are easy to confuse without reading the descriptions carefully.

Naming Consistency4/5

The vast majority of tools follow a clear verb_noun pattern such as create_, get_, list_, search_, send_, and delete_. A handful of noun-phrase outliers like linkedin_analytics, mutual_connections, top_message_correspondents, and what_needs_attention break the pattern, so it is highly consistent but not perfect.

Tool Count1/5

64 tools is an extreme count, far beyond the typical well-scoped 3-15 tool range and even beyond the 25+ threshold for 'too many'. While the server covers many integrations, this many tools creates a heavy navigation burden and would be better split into focused servers per domain.

Completeness3/5

Core graph/CRM operations and read-side integration coverage are strong, with search, get, list, and create tools across most domains. However, there are notable dead ends: no delete_calendar_event, no tracker management beyond create_tracker, and set_follow_up explicitly lacks a read-back query tool, so some natural user requests cannot be completed through the toolset.