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Alir3z4

tb-query

by Alir3z4
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
TB_QUERY_EVENTS_PATHNoPath to a default directory containing TensorBoard event files. When set, the MCP server exposes an event_files resource listing event files from this directory.

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
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
queryA

Query scalar data from a TensorBoard event file.

This will return all the data inside the Tensorboard event files. The result for a long training is very big and can consume all your context limit if not properly filtered by tags and start_step and end_step.

  • To know the available event files you can use find_events tool to get all the available event files.

  • To know the available tags of a Tensorboard file, you may use list_tags which gives you all the available scalar tags in the event file.

  • To know all the available training steps for each tag, you can use tag_steps tool.

  • Use start_step and end_step to ask for a range of data.

  • Try to use tags explicitly, otherwise the output can be huge.

list_tagsA

Get all available scalar tags from a TensorBoard event file with optional filtering.

  • To search/filter the returning tags, you can pass a list of string to the filters parameter, which will only return the tags that contain those filter strings.

find_eventsA

Find all TensorBoard event files in the specified directory and its subdirectories.

Use this endpoint when you don't know the available events or user has not given you any event file name to analyze.

tag_stepsA

Get the steps for each specified tag from a TensorBoard event file.

  • Pass tags to get return steps for only specified tags, otherwise the output can be quite large.

tag_statsA

Get statistical measures (min, max, mean, std) for each specified tag's values.

correlationB

Calculate the correlation between scalar tags in a TensorBoard event file

It will provide correlation for the given tag(s) with other tags in the tensorboard data.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription
EventFilesShows all the event files available since last training run.
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.9/5.0

Scored across 6 tools

Disambiguation5/5

Each tool has a distinct purpose: finding files, listing tags, querying data, getting steps, statistics, and correlations. No two tools overlap in functionality.

Naming Consistency3/5

Tools use a mix of verb_noun (find_events, list_tags) and noun_noun (tag_steps, tag_stats) patterns, plus single-word verbs (query) and nouns (correlation). The pattern is inconsistent but still readable.

Tool Count5/5

With 6 tools, the server covers all necessary operations for querying TensorBoard scalar data without being excessive or minimal.

Completeness4/5

The tool set provides a complete workflow: discover files, list tags, get steps, query data, compute statistics, and correlate. Minor gaps like comparing runs are missing but not critical.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources