speedread MCP server
io.github.brennengreen/speedread·v0.1.0Token-efficient code reading for coding agents: symbol-aware, budgeted, diff-aware reads.
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If you have run it, two minutes of your experience saves the next person an afternoon.
speedread tools
No tool declarations could be read from the package source. They show once the server is installed.
Public scan report
scanner v0.1.9 · 2026-09-27 · same rubric, same numbers if you re-run it
- –Code scanpackage could not be scannedn/a
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 0 days ago15/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year8/10
How speedread compares
Against every graded MCP server, from our own scans and uptime checks. Updated after each scan run.
- Safety score
- 88/100
- higher than 75% of 48,255 servers
Install speedread in Claude Code, Cursor or VS Code
The registry entry has no remote endpoint or installable package.
What the publisher says
From the speedread repository's README, as published. We do not edit it. Read it on GitHub
speedread
Token-efficient code search and navigation for AI coding agents. speedread is an MCP server and CLI that gives Claude Code, GitHub Copilot, Codex, Cursor, Gemini CLI and other agents the part of a codebase a question needs, within a token budget: the function around each search hit, a large file's skeleton, a symbol's callers and implementations, or only what changed since the last read. Not whole files and bare grep hits.
Read is the wrong abstraction for coding agents. Agent code reading should be adaptive, stateful, symbol-aware and token-budgeted instead of byte-oriented. Every model call re-sends the system prompt, tool definitions and conversation so far (18–21k tokens before any code, in our evals), so an agent's cost is driven more by round trips than by bytes. ripgrep returns matches and cat returns bytes, so the agent asks again: open the file, find the function, search for the next hop. speedread returns the minimum useful unit of code for the question, with enough structure that the next call often isn't needed.
One of the ten code-question tasks, replayed from its recorded eval transcripts at recorded speed; all three trials of each condition behaved identically. The built-in grep answers with a file name, so the agent has to ask again, twice. speedread's search answers with the matching lines under their enclosing declaration. This is the second-largest saving of the ten tasks; two tasks came out about 1% worse, and across all ten, input tokens fell 35%. Full interactive report: brennengreen.github.io/speedread (also self-contained in demo/index.html), generated by demo/build.py from evals/results/.
Measured, not hand-waved
Real agents on real repositories, with the same model (claude-sonnet-5) and harness (GitHub Copilot CLI) in both arms: built-in tools vs speedread as the reader. Every trial, transcript, grader and diff is committed, including the workloads where speedread didn't help.
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
speedread: common questions
- Is speedread MCP server safe?
- Yes, by our scan: it is graded A (88/100). Read the speedread safety report
- Does speedread need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is speedread maintained?
- The last commit was 38 days ago (2026-09-27). The latest release is v0.1.0.