NeuralMind v4.21.0 — an agent that can see NeuralMind: MCP server instructions, a six-tool default list, and the leading file’s code in the prompt block

The task-outcome pilot ran the same agent on 20 real bug fixes with and without NeuralMind: 120 runs. NeuralMind made no difference: median cost per task +0.0% (95% CI −5.2% to +8.6%), 18 of 20 tasks solved either way. Replaying what NeuralMind gave the agent in those runs, with no agent and no API cost, showed why:

• The agent never saw what the tools were for. Claude Code defers MCP tools: the model sees their names and has to look a tool up before it can call it. It saw 28 names, no descriptions and no word on when to use any of them, and called none in 60 runs.
• The prompt block named the right file and the agent searched anyway. The UserPromptSubmit hook named the file the fix changed in 17 of 20 tasks. The agent made a median of 4 search or read calls before its first edit, with NeuralMind or without it.

This release changes both, and the agent’s behaviour on those 20 tasks didn’t change: an exploratory rerun with this release found the agent still called no NeuralMind tool, and searched as much before its first edit as without NeuralMind (measured after). What the release does change, and what it doesn’t, is below.

What changed

The MCP server says what it’s for

The server now sends instructions in its initialize reply. Claude Code shows a server’s instructions to the model even while that server’s tools are deferred. They say: to find where something is implemented, or the code behind a bug from its symptom, call neuralmind_search before Grep or Glob; neuralmind_query to understand how a part works; neuralmind_skeleton, neuralmind_impact and neuralmind_query_decisions for structure, dependents and recorded decisions; and to start from a prompt’s NeuralMind block when it has one.

Asked, before calling anything, which tool it would use to find the code behind a bug, Claude Code 2.1.287 on Sonnet 5.5 answered “NONE” with v4.15.1’s server, and “neuralmind_search”, quoting the instructions, with this one.

tools/list offers six tools by default

neuralmind_search, neuralmind_query, neuralmind_skeleton, neuralmind_impact, neuralmind_wakeup and neuralmind_query_decisions. The other 22 (build, stats, benchmark, savings, the synapse tools, health, review, compliance report, document ingestion, and the rest of the decision tools) are still served: a client that calls one by name gets the same answer as before. They’re just not in the list an agent chooses from.

The prompt block shows the leading file’s code

When one file clearly leads the prompt’s matches (their summed score is at least twice the next file’s), the block the UserPromptSubmit hook adds (and the Hermes plugin’s pre_llm_call hook) now quotes that file’s best-matching function or class:

Code matching this prompt:
- neuralmind/secret_scan.py: redact_text() L479, scan_text() L402, …
- neuralmind/output_cache.py: …
Most matches are in neuralmind/secret_scan.py. Its best match, L348-L377 (read the file before you edit it):
```
def _scan_line(…):
    …
```

• From the working tree, so it is what the agent would read; from the symbol’s first line to the line before the next symbol the graph records in that file, at most 40 lines.
• Secrets are redacted with the same scanner neuralmind last uses.
• No lead, no code: with no file 2:1 ahead, the block is the file list as before. NEURALMIND_RECALL_EXCERPT=0 turns the excerpt off.

evals/task_outcome/replay.py: the hook, scored offline, in CI

It exports each of the pilot’s 20 tasks at the fix’s parent commit, builds the index, sends the task’s prompt through neuralmind _hook prompt-submit, and scores the block against the fix’s own diff. A new workflow, Prompt-recall replay, runs it on every pull request that touches the recall path and fails below its floors. Measured on this release:

The last row is the honest limit of the excerpt. The best-matching symbol is usually in the right file, and usually not the function the fix touched: a symptom reads like what the code is about, not where it breaks. The excerpt’s gate was set on these same 20 prompts, so 7 of 9 is a ceiling, not an estimate.

Measured after: the agent’s behaviour didn’t change

An exploratory rerun (not pre-registered) ran the pilot’s 20 tasks again, 3 times each, with this release in the NeuralMind arm. Same model, Claude Code version, prompts and budget, against the pilot’s baseline runs:

• The instructions reached the model and didn’t change its choice. Asked, it names neuralmind_search; given a real bug, it used Grep and Read in all 60 runs and never called ToolSearch, which a deferred tool needs first.
• The excerpt didn’t replace the agent’s own search. On the 9 tasks where the block quoted code, the agent made a median of 4.0 search or read calls before its first edit, against 3.0 without NeuralMind.
• Cost: median −2.1% per task against the baseline (95% CI −6.9% to +6.5%, p = 0.34), 18 of 20 tasks solved in each arm. No difference shown.

On these tasks an agent finds the file to fix in about four searches with or without help, which leaves little to save. Whether NeuralMind changes the outcome on a larger, unfamiliar repository with harder fixes is not measured yet.

What the agent actually sees post-install

Upgrading

• If an agent or script relies on a tool outside the six, by name, it keeps working. If an agent needs to see it to choose it (Cursor’s tool picker, say), set NEURALMIND_MCP_TOOLS=all, or name the tools, in the MCP server’s environment.
• Nothing to rebuild: the excerpt reads the index you have.

New environment variables

Use cases

• Existing: NeuralMind in Claude Code. The agent gets the tool it should reach for named in the server’s instructions and, on prompts where NeuralMind is confident, the code itself.
• New: check what your agent is handed before paying for agent runs. python -m evals.task_outcome.replay scores what the hook shows for a set of bug-fix tasks against their real fixes, offline. The harness that measures the outcome (evals/task_outcome/run.py) can then measure whether it mattered.