How do I use retrace-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@retrace-mcp search my screen history for 'quarterly planning'" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

retrace-mcp

by calvingunther66

Overview Schema Related Servers Score Discussions

Python

Local

retrace-mcp

Read-only MCP server over the local Retrace screen-history database, so Claude can search what has been on your screen.

Six tools. No write path anywhere in the code — not a disabled one, none.

Read SECURITY.md before connecting this to anything. It exposes the OCR'd text of everything you have had on screen, and the prompt-injection section describes a risk the deny-list does not cover.

search_text("quarterly planning")
  2026-02-11T09:00:00-08:00  Google Chrome   Quarterly planning - Docs
    ...Quarterly <mark>planning</mark> document. Revenue targets for the...

Requirements

macOS with Retrace installed, uv, and the claude CLI. Python ≥3.11 (uv provisions its own, so a system Anaconda will not be picked up). Built and verified against Retrace 0.8.7.

Related MCP server: Discord Message Finder MCP

Install

git clone https://github.com/calvingunther66/retracemcpclaude.git
cd retracemcpclaude
bash install.sh

Installs systemwide (claude mcp add --scope user) — available in every Claude session on the machine. The installer creates your config from the example, verifies your database schema matches what the server expects, runs the test suite, and only then registers the server.

Remove it at any time:

claude mcp remove retrace

Tools

Every tool takes an optional limit (default 20, maximum 100). Timestamps cross the boundary as ISO 8601 local time in both directions — pass 2026-02-11 or 2026-02-11T09:30:00, get back 2026-02-11T09:30:00-08:00. Raw epoch milliseconds never leak out. Every tool applies the deny-list inside its SQL.

Tool	Returns
`search_text(query, start?, end?, bundle_ids?, limit?)`	Full-text search over screen OCR. One hit per frame with a `<mark>`-highlighted snippet, app, window title, URL, and the video id / frame index.
`list_segments(start?, end?, bundle_id?, limit?)`	Capture segments — one contiguous stretch in one app window — with start, end and duration.
`get_frame(frame_id, limit?)`	One frame with its OCR text boxes and parent segment.
`app_usage(start, end, limit?)`	Time per app across a window, ordered descending, with display names.
`list_tags(limit?)` / `segments_by_tag(tag, limit?)`	Retrace's segment tags.

search_text accepts FTS5 syntax: invoice, "exact phrase", budget NEAR/5 forecast, sched*.

OCR box geometry is normalised 0.0–1.0, not pixels. Multiply by the video's width and height for pixel coordinates.

processing_queue is never queried — it is the hot live OCR queue.

Configuration

retrace_mcp.toml in the repo root, created from retrace_mcp.example.toml on install. Changes take effect on restart.

[database]
path = "~/Library/Application Support/Retrace/retrace.db"

[privacy]
exclude_bundles         = ["com.apple.Terminal", "com.1password.1password", ...]
exclude_url_patterns    = ["chase.com", "vanguard.com", ...]
exclude_window_patterns = ["1Password", "Online Banking", ...]
exclude_hidden_segments = true
skip_redacted           = true

Key	Effect
`exclude_bundles`	App bundle IDs hidden from every tool.
`exclude_url_patterns`	Substrings matched against the segment's browser URL.
`exclude_window_patterns`	Substrings matched against the window title.
`exclude_hidden_segments`	Honour Retrace's own `hidden` tag.
`skip_redacted`	Drop rows whose snippet is `rtx1.` redaction ciphertext.

Why URL and window patterns exist. Excluding applications is not enough on its own. Banking, webmail and health portals run inside a browser, under an allowed bundle ID — a bundle-only deny-list leaves every one of those pages fully searchable. The URL and window lists are what actually close that gap, and they are the ones worth maintaining.

Filters are applied inside each query, never as a post-filter. Post-filtering leaks through LIMIT: an excluded row would consume a result slot, and its text would have been read into the process regardless. % and _ in patterns are escaped and matched literally.

Defaults ship excluding terminals, which costs you the ability to ask "what was that command I ran earlier". Terminals show secrets in the clear — keys echoed by a command, tokens in an error message, anything pasted — and all of it is OCR'd into plaintext. Delete the terminal block if you want that capability back.

With no config file present, a built-in deny-list applies. git clone && run can never produce zero filtering.

Development

uv venv --python 3.12 && uv pip install -e . pytest
python -m pytest tests/test_tools.py -q

Tests build a synthetic database — you do not need Retrace, or a Mac, to work on this. The fixture deliberately contains a terminal session with a fake API key, a password-manager window, and a banking URL in a browser, so the deny-list tests assert real behaviour rather than passing against data that never had anything to hide.

tests/test_live.py is separate and runs against a real database while Retrace is capturing: concurrency under write load, torn reads, WAL growth, and write refusal. See CONTRIBUTING.md.

Useful scripts:

Script	Purpose
`tools/verify_schema.py`	Check your database against what the server expects. Run this before filing a bug.
`tools/make_fixture.py`	Regenerate the synthetic test database.
`tools/snapshot.sh`	Consistent read-only copy of a live database, via SQLite's backup API.
`tools/exercise_tools.py`	Drive all six tools over real MCP stdio.

How it reads the database safely

Rule	Why
`file:<path>?mode=ro`, never `immutable=1`	The capture daemon writes continuously. `immutable` lets SQLite skip locking, producing torn reads that fail silently.
`PRAGMA query_only = ON`	Belt-and-braces alongside `mode=ro`.
`busy_timeout` 5000 ms, 2 retries	FTS5 index writes hold locks during capture.
Fresh connection per call	Nothing is held across calls.
`isolation_level=None`	A long-lived read transaction blocks the WAL checkpointer and the WAL grows unbounded.
No write statement in the package	Enforced by an AST-parsing test.

Verified against a live database under active capture: 200 concurrent calls across 8 threads with zero errors, WAL flat throughout, and CREATE, DELETE, UPDATE, INSERT and journal-mode changes all refused.

Notes on the schema

Observations from a Retrace 0.8.7 install that are not obvious from the outside, and cost time to discover:

PRAGMA user_version reads 0. Migration state lives in a schema_migrations table, not in user_version.
Timestamps are epoch milliseconds, not seconds.
OCR box coordinates are normalised 0–1, not pixels.
searchRanking is a content-ful FTS5 table (text, otherText, title). Were it contentless, snippet() would raise.
Roughly half of frames are not in the search index — they have no doc_segment row and are invisible to search_text by design.
The schema is much larger than the six tools use. audio, transcript_word, event, summary, segment_comment and in_page_url_text all exist. No tool here reads them. Some contain material more sensitive than screen OCR — audio transcripts and calendar participants — so think carefully before adding one that does.
Video segments live at chunks/YYYYMM/DD/<13-digit-epoch-ms>, extensionless but real MP4.

Other Retrace versions may differ. tools/verify_schema.py will tell you.

License

MIT — see LICENSE.

Not affiliated with Retrace.

Available Tools

6 tools

app_usageA

Read-onlyIdempotent

Time spent per app over a window, from summed segment durations.

Args: start: ISO 8601 local start of the window. end: ISO 8601 local end of the window. limit: 1-100, default 20.

ParametersJSON Schema

Name	Required	Description	Default
`end`	Yes
`limit`	No
`start`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A3.5/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about how the data is computed ('from summed segment durations') and states the limit bounds. However, it does not disclose return format, pagination, or behavior when no data exists, so it does not go far beyond annotations.

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?

The description is compact, front-loaded with the core purpose, and includes a clearly formatted Args block. Every sentence contributes necessary information without redundancy or fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity, the presence of an output schema, and strong annotations, the description covers the essential inputs and purpose. It does not explain when to use it vs siblings, but for a straightforward query tool that may be acceptable. The description is sufficiently complete 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.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description bears the full burden of explaining parameters. It does this well: start and end are defined as 'ISO 8601 local start/end of the window', and limit is specified as '1-100, default 20'. The only minor issue is that the schema lists limit's default as null while the description says 20, a slight inconsistency in default value specification.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool reports 'Time spent per app over a window', specifying both the resource (apps) and the scope (time window). It does not explicitly use a verb like 'get' or 'list', but the intent is unambiguous. It does not differentiate from sibling tools, but its focus on app usage is distinct enough.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

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 any context or exclusions, such as 'use this for aggregated usage, use list_segments for raw segments'. The only implied context is a time window, but no explicit usage scenario is described.

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

get_frameA

Read-onlyIdempotent

Fetch one frame with its OCR text boxes and parent segment context.

Box geometry (leftX, topY, width, height) is expressed as normalised fractions of the frame, in the range 0.0-1.0 — not pixels. Multiply by the video's width/height to get pixel coordinates.

Args: frame_id: The frame's numeric id. limit: Max OCR node boxes to return, 1-100, default 20.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No
`frame_id`	Yes

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already signal read-only, idempotent, and non-destructive behavior. The description adds a non-obvious output convention: coordinates are normalized fractions (0.0–1.0) rather than pixels, and it clarifies the limit semantics. It does not contradict the annotations, so the disclosure is solid.

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?

The description is front-loaded with a one-sentence purpose, followed by a necessary coordinate-normalization caveat and a compact Args block. No filler or redundant repetition of schema fields.

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 two-parameter frame fetch with an output schema and strong annotations, the description covers the key semantics: what is returned, the coordinate system, and limit constraints. It does not need to explain return structure because the output schema exists, and no missing usage context undermines its completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates fully: frame_id is explained as a numeric id, and limit is given a range (1-100) and default (20) beyond the schema's plain integer/null type. The only minor issue is that the schema specifies default null while the description says 20, but the added guidance is substantial.

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 opens with 'Fetch one frame with its OCR text boxes and parent segment context,' naming a specific verb (fetch), resource (frame), and payload. This clearly distinguishes it from list/search siblings like list_segments, segments_by_tag, and search_text. It is not a tautology or vague.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit statement of when to use this tool versus alternatives such as list_segments or search_text. The description implies 'fetch a single frame,' but it does not state prerequisites, fallback cases, or when not to use it. This is the weakest dimension.

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

list_segmentsA

Read-onlyIdempotent

List capture segments (one contiguous stretch in one app window).

Args: start: Optional ISO 8601 local lower bound on segment start. end: Optional ISO 8601 local upper bound on segment start. bundle_id: Optional single app bundle ID to filter to. limit: 1-100, default 20.

ParametersJSON Schema

Name	Required	Description	Default
`end`	No
`limit`	No
`start`	No
`bundle_id`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true and idempotentHint=true. The description adds parameter semantics and the definition of a segment, but does not disclose return format, ordering, or pagination behavior beyond the limit parameter. No contradictions with annotations.

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?

One-sentence purpose plus a compact Args block. Every line is informative, no redundancy, well-structured.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an output schema present and all parameters explained, the description is essentially sufficient for a read-only list operation. It slightly lacks comparative usage context with sibling tools, but that is not required for functional completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has 0% description coverage, but the description fully explains all four parameters: start/end as ISO 8601 bounds on segment start, bundle_id as a filter, and limit with range/default. This fully compensates for the schema gap.

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 opens with a clear verb+resource statement ('List capture segments') and defines the domain concept in parentheses, distinguishing it from sibling tools that list tags or search text. This is a specific, unambiguous purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool vs alternatives like segments_by_tag. The parameter descriptions imply it can be filtered by app, but no alternative/exclusion criteria are stated.

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

list_tagsA

Read-onlyIdempotent

List segment tags and how many (visible) segments carry each.

Args: limit: 1-100, default 20.

ParametersJSON Schema

Name	Required	Description	Default
`limit`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds important context beyond annotations: counts are for 'visible' segments only, and the limit range/default is specified. This clarifies scope and constraints that annotations do not convey.

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?

The description is minimal and front-loaded: a single clear sentence stating the tool's purpose, followed by a compact parameter specification. Every sentence adds value with no redundancy.

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 simple tool with one optional parameter and existing annotations plus an output schema, the description covers all necessary aspects: what it does, the visibility scope, and parameter constraints. It is fully adequate for agent invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description fully compensates: it documents the limit parameter's range (1-100) and default (20), including the actual default which differs from the schema's null. This is essential for correct invocation.

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 lists segment tags and counts visible segments per tag. It distinguishes from siblings like list_segments (which likely lists segments) and segments_by_tag (which likely lists segments for a specific tag), as it focuses on tag-level aggregate counts.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies use case (viewing tag distribution with visible segment counts) but does not explicitly state when to prefer this over alternatives or exclude other scenarios. No sibling comparison is provided, so guidance is left to inference from the verb and resource.

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

search_textA

Read-onlyIdempotent

Full-text search across OCR'd screen content.

Args: query: FTS5 match expression, e.g. invoice, "exact phrase", budget NEAR/5 forecast, sched*. start: Optional ISO 8601 local lower bound on frame time. end: Optional ISO 8601 local upper bound on frame time. bundle_ids: Optional allow-list of app bundle IDs to search within. limit: 1-100, default 20.

ParametersJSON Schema

Name	Required	Description	Default
`end`	No
`limit`	No
`query`	Yes
`start`	No
`bundle_ids`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.1/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, covering safety. The description adds FTS5 query syntax and parameter defaults, but no behavioral traits such as pagination or return format. It does not contradict annotations, and the bar is lower due to rich annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is structured with 'Args:' and uses clear, bullet-like lines. It is slightly verbose for the purpose but every sentence adds value, especially the query examples. No wasted words.

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?

Given that an output schema exists, return values do not need to be described. The description covers all parameters and uses the FTS5 syntax detail effectively. It is complete for a search tool with good annotations and an output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description fully compensates. It explains each parameter: query with concrete FTS5 examples, start/end as ISO 8601, bundle_ids as allow-list, and limit range (1-100, default 20). This goes far beyond the raw schema.

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 'Full-text search across OCR'd screen content,' using a specific verb and resource. This distinguishes it from siblings like list_segments or get_frame, which target different aspects.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage context (searching screen content) but does not explicitly mention when to use this tool instead of alternatives. No exclusions or alternative names are provided, so context is clear but not comparative.

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

segments_by_tagA

Read-onlyIdempotent

List segments carrying a given tag name.

Args: tag: Exact tag name, as returned by list_tags. limit: 1-100, default 20.

ParametersJSON Schema

Name	Required	Description	Default
`tag`	Yes
`limit`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
No output parameters

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already indicate a safe read-only, idempotent operation. The description adds that tag matching is exact and that limit is a 1-100 range with a default of 20, which are useful behavioral details beyond the schema.

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?

The description is extremely compact, leading with a one-sentence purpose and followed by a minimal Args list. No extraneous information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given only 2 parameters and the presence of an output schema, the description covers the purpose, parameter semantics, and prerequisite workflow. It lacks explicit guidance on when to use alternatives, but is otherwise sufficient for a simple filtered read tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has no property descriptions (0% coverage). The description explains that tag must be an exact value from list_tags and clarifies limit's range/default. However, the stated default of 20 conflicts with the schema's default of null, which could confuse an agent.

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 states 'List segments carrying a given tag name,' which clearly identifies the verb (List), resource (segments), and scope (by tag). It differentiates from sibling tools like list_segments (unfiltered) and list_tags (tags only).

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 instructs that the tag must be an exact name as returned by list_tags, establishing a clear prerequisite and workflow. It also specifies the limit range, but does not explicitly contrast with list_segments or provide exclusion criteria.

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.

6 tool updatesv0.1.0Aug 6, 2026
- First observedapp_usage
- First observedget_frame
- First observedlist_segments
- First observedlist_tags
- First observedsearch_text
- First observedsegments_by_tag

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

A4/5.0

Disambiguation5/5

Each tool targets a distinct query type: segment listing, app usage aggregation, tag listing, segments-by-tag, text search, and frame retrieval. The purposes are clearly differentiated, with no apparent overlap.

Naming Consistency3/5

Tool names follow a mix of conventions: list_* and get_* and search_text are verb_noun, while app_usage and segments_by_tag are noun phrases. This mixed pattern is still readable but lacks a single consistent style.

Tool Count5/5

Six tools is well within the ideal range for a focused query-only domain. Each tool serves a distinct analytical purpose without redundancy.

Completeness4/5

The surface covers the core query needs: segment listing, app usage, tag filtering, text search, and frame detail. Minor gaps exist, such as no dedicated get_segment tool or tag management, but these are not critical for the likely read-only intent.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

Related Servers

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Related MCP Connectors

forkast-mcp-docs A
io.github.ForkastPredict
MCP server for querying Forkast documentation
Documentation Access Search Developer Tools
42
VocciOAuth
ai.vocci
Read-only MCP access to authorized Vocci sessions, notes, files, and memory search.
Note Taking Knowledge & Memory
2
LLMemoryOAuth
io.github.meir-may
Search your AI chat history (ChatGPT, Claude, Codex) from any MCP client. Remote, private, read-only
Search Knowledge & Memory
2
The Quiet Protocol Growth Offense MCP A
com.thequietprotocol.www
Read-only MCP server for The Quiet Protocol's engines, benchmarks, proof, and business data.
Databases Research & Data Finance
292

Related MCP Servers

Atlas
Documentation Access Search RAG Systems
moxellabs
A
license
Not graded
quality
A
maintenance
Local-first MCP server for querying multi-repo engineering documentation artifacts from a SQLite corpus.
Updated 13 days ago (2026-08-25 01:07 UTC)
10
AGPL 3.0
Discord Message Finder MCP
Communication Search
DonghwanJeong
F
license
A
quality
C
maintenance
Read-only MCP server for finding Discord messages. It enables searching guild messages, locating messages from jump URLs, and reading context around results.
Updated 3 months ago (2026-06-14 10:33 UTC)
7
1
-
local-dev-tools
Databases Developer Tools Version Control
emmahuang-ee
A
license
Not graded
quality
C
maintenance
A read-only MCP server providing SQLite querying, port checking, and git diff summary tools for local development.
Updated a month ago (2026-07-31 02:12 UTC)
MIT
chist
Search Databases Knowledge & Memory
hiropon164
A
license
Not graded
quality
B
maintenance
MCP server for unified full-text search across chat histories from Claude Code, Codex, Cursor CLI, and Antigravity CLI, using SQLite FTS5. Provides read-only tools to search sessions, list conversations, and retrieve session details.
Updated a month ago (2026-08-03 10:26 UTC)
MIT

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/calvingunther66/retracemcpclaude'

If you have feedback or need assistance with the MCP directory API, please join our Discord server