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toolahead

by michael-ra
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Stop waiting for tools

Agents normally work serially:

reason → call tool → wait → inspect → reason → call tool → wait

ToolAhead learns which calls usually follow each other. It starts the likely next call while the model is still working:

Agent       inspect result ───── reason ───── request next tool ── result
ToolAhead                  └──── run predicted tool ──────────────┘

The agent still calls ordinary MCP tools. If no matching result is ready, the tool runs normally. If ToolAhead prepared the exact call against the exact same files, the result returns immediately from memory.

ToolAhead also hides a second kind of waiting: declared dev servers and other slow prerequisites start right after the first edit — while the model is still reasoning — so they are already warm and health-checked when the test or e2e call arrives. In a real Next.js session that turned a 3.5s dev-server wait into 0.45s. See Pre-warming external services.

Related MCP server: token-ninja

See it run

Codex: the same task with and without ToolAhead

This is a 1× timeline from a matched Codex pair using the real API and separate copies of the same project. The protocol and paired Codex/Claude measurements are in BENCHMARKS.md.

Full recorded runs

These 1× recordings show ToolAhead handling the complete workflow: list, search, read, edit, write, test, result validation, and reuse.

Codex CLI

Claude Code

Install

Once published on PyPI:

uvx toolahead --help
# or
python3 -m pip install toolahead

From a local checkout today:

git clone https://github.com/michael-ra/toolahead.git
cd toolahead
uvx --from . toolahead --help

Requirements: Python 3.11+, macOS or Linux, and an authenticated Codex CLI, Claude Code, or Google Antigravity installation. watchdog is optional.

Quickstart

Run these commands inside the project you want to accelerate:

# Connect your agents. The default is hooks-only: ToolAhead replaces
# NOTHING — your agent keeps its native tools, and learning, service
# pre-warming, route warming, and Bash replay all ride on lifecycle hooks.
uvx toolahead init --agent both --project .
# Want Read/Search replay hits too? --replay-tools registers the ToolAhead
# MCP tools; --strict additionally hides the native analogs (maximum hits).
# Google Antigravity user? Add --agent all, or run: uvx toolahead init-antigravity

# Allow this exact test command to run ahead and be reused.
uvx toolahead allow "python3 -m pytest" --project .

# Optional: declare your dev server in toolahead.toml, then approve it once
# so it can be pre-warmed while the model reasons.
uvx toolahead trust --project .

# Start ToolAhead in the background for this workspace.
uvx toolahead serve --workspace .

Then start your agent in a second terminal.

Codex CLI:

codex

Claude Code:

ANTHROPIC_BASE_URL=http://127.0.0.1:4242 claude

Google Antigravity: init-antigravity covers both surfaces, because they discover hooks differently.

  • The agy CLI reads lifecycle hooks only from plugins. ToolAhead writes one to ~/.toolahead/agy-plugin/ and installs it automatically when agy is on your PATH (otherwise it prints the agy plugin install command). Verified against CLI 1.1.11: the daemon receives Antigravity's native tool events with source: antigravity-hook. The plugin is global on purpose and carries no project URL — each hook reports the workspace it ran in, and a daemon serving a different project refuses it, so one install covers every project.

  • The IDE additionally uses the workspace files .agents/hooks.json and .agents/mcp_config.json, which init-antigravity also writes. Run /mcp in the prompt panel once to confirm the toolahead server is enabled.

The hooks observe Antigravity's native tools, so learning, service pre-warming, and route warming work without the ToolAhead MCP tools.

Antigravity support remains experimental, and one limitation is worth knowing before you try it. On CLI 1.1.11, agy --print reports workspacePaths: [] and runs commands in its own scratch directory (~/.gemini/antigravity-cli/scratch) rather than in your project. ToolAhead therefore correctly does nothing there — there is no project state to accelerate. The hook path itself is verified: the daemon receives Antigravity's native tool events with the right tool mapping and model. What is not verified is an end-to-end speedup on Antigravity, because the CLI's print mode never puts the agent in the project. The IDE is expected to behave differently; if you use it, feedback on whether pre-warming engages is very welcome.

The measured speedups quoted in this README come from Claude Code and Codex sessions.

See live timing and cache statistics at any time:

uvx toolahead status

Rerun toolahead init after upgrading ToolAhead. It refreshes ToolAhead's project files without changing unrelated Codex, Claude, or MCP settings.

One clear set of tools (opt-in)

The MCP tools are opt-in (--replay-tools). Everything except Read/Search replay works without them: lifecycle hooks observe the native tools, drive learning and pre-warming, and (on Claude Code and Codex) replay allowlisted Bash commands transparently. Registering the ToolAhead tools adds the one thing hooks cannot do — serving prepared file-read and search results — because only the tool that owns a call can answer it from memory.

How much each agent reports natively differs, and that decides what can be learned without the MCP tools:

Agent

Native tool events the hooks receive

Claude Code

Bash, Edit, Write, MultiEdit, NotebookEdit, Read, Grep, Glob

Antigravity

run_command, view_file, write_to_file, replace_file_content, multi_replace_file_content, grep_search, find_by_name

Codex CLI

Bash and apply_patch only

Codex is the exception worth planning around: mutation-triggered pre-warming and command replay work there exactly as elsewhere, but read and search sequences never reach the hooks, so learning those needs --replay-tools.

MCP tool

Familiar input

Can run ahead

Behavior

list_files

pattern, path, limit

✓

Lists matching files

search

pattern, path, glob, output mode

✓

Searches file contents

read_file

file_path, offset, limit

✓

Reads a file with line numbers

edit_file

file_path, old_string, new_string, replace_all

—

Makes an exact edit and starts the next prediction

write_file

file_path, content

—

Creates or replaces a file and starts the next prediction

run

command, description

✓

Runs approved tests, builds, and linters

The agent never sees cache wrappers or duplicate JSON. ToolAhead keeps cache timing in hidden MCP _meta; prepared and normal calls return the same text, errors, and exit codes.

Why --strict matters

Showing two equivalent Read tools forces the model to choose between duplicate options, wastes prompt space, and makes selection less reliable. Strict mode keeps one set:

  • Claude Code's project settings hide native Read, Grep, Glob, Edit, and Write; the six ToolAhead MCP equivalents take their place.

  • Codex sees the same six tools and instructions to use them. Strict mode redirects native apply_patch to edit_file so edit→test learning stays intact. Codex's general shell remains available when needed; explicitly allowed Bash tests can still reuse prepared results.

  • Tool names and field conventions stay close to the native coding-agent tools. Descriptions are intentionally short to reduce the tokens sent to the model.

Omit --strict if you want to keep all native file tools visible while trying ToolAhead. Switching back is safe: every toolahead init writes the routing that its flags describe, so a later run without --strict restores the native file tools and removes the strict marker along with the MCP registration.

Predictions can be wrong. Returned results cannot.

ToolAhead is free to guess what comes next, but it returns prepared work only when the requested call and current files are exact matches.

flowchart LR
    A[Previous tool or turn start] --> B[Predict next exact call]
    B --> C[Read-only worker or disposable checkout]
    A --> D[Agent keeps reasoning]
    C --> E{Exact call + fresh SHA-256 input match?}
    D --> E
    E -->|match| F[Return prepared result from RAM]
    E -->|no match| G[Execute the MCP call normally]

  • List, Search, and Read results are tied to the exact request and the relevant file contents.

  • Command results are tied to the exact command and a fresh hash of the whole workspace.

  • Commands run ahead only in a disposable workspace copy.

  • A prepared result is returned only when the real workspace still matches the copy used to create it.

  • Wrong predictions, background-process failures, expired results, and timeouts automatically fall back to a normal tool execution.

  • Cache entries store stdout, stderr, and exit code—not a model-generated summary.

ToolAhead learns tool sequences locally. The reliable signal is the previous tool finishing; visible commentary can offer an earlier hint when an agent provides it. Private chain-of-thought is never required.

Latest file change wins

ToolAhead does not need to guess which edit will be the last one. Every successful Edit or Write increases a simple workspace version number:

edit version 1 ── start predicted tests
edit version 2 ── stop version 1 ── restart tests on version 2
edit version 3 ── stop version 2 ── keep only the version 3 result

  • A running command for an older file version receives SIGTERM as a process group, then SIGKILL if it does not stop promptly.

  • The pending command is restarted for the newest file version even when another edit arrives before the test request.

  • Writes arriving within 50 ms are grouped before work starts. Configure the window with PREFETCH_MUTATION_DEBOUNCE_MS; set it to 0 to disable grouping.

  • Outdated results are never inserted into the current cache. Fresh SHA-256 validation remains the final replay condition.

  • Failed file changes do not increase the workspace version.

In plain terms: after every successful file change, ToolAhead starts the likely next safe call. Nearby changes are grouped, and a newer change always replaces work started for an older file state.

Which commands can be reused

ToolAhead may return a prepared command result instead of running the command again only when that exact command is listed in .prefetch-replay.json:

{
  "commands": [
    "python3 -m pytest",
    "npm test"
  ]
}

Use the CLI instead of editing the file by hand:

toolahead allow "python3 -m pytest" --project .

The allowed-command list updates without restarting ToolAhead. It rejects shell chains, pipes, redirects, substitutions, installers, and arbitrary commands; recognized test/lint families include unittest, pytest, npm/yarn tests, Go, Cargo, Make, Jest, Vitest, Ruff, ESLint, TypeScript, and mypy.

Prioritize known failures without weakening the result

Use the test runner's explicit full-suite mode when available. For pytest, pytest --ff runs the last failures first and then the rest of the suite; ToolAhead can learn and reuse that exact command normally. Focused modes such as pytest --lf or Jest --onlyFailures are useful quick checks, but ToolAhead never substitutes their partial result for a requested full-suite result.

Pre-warming external services (optional)

ToolAhead accelerates two different things and never mixes them up:

  • Result speculation prepares an answer ahead of time. It is limited to calls whose output is a pure function of the workspace files — List, Search, Read, and allowlisted test commands — because the content hash proves the result is identical.

  • Pre-warming starts slow, long-lived prerequisites ahead of time — a dev server, a browser. No result is ever served from memory here; the win is purely the eliminated startup latency.

Commands whose output depends on a running service (Playwright against a dev server, integration tests against a database) belong to the second category: a file hash cannot prove their results equal, because server state — hot-reload timing included — is not stored in files. Declare them in an optional toolahead.toml at the workspace root:

[services.dev-server]
command = "npm run dev"
ready.port = 3000        # or ready.http = "http://…" / ready.command = "curl -sf …"
timeout = 30             # seconds to wait for readiness (default 30)
prewarm = "mutation"     # "mutation" (default) | "start" | "manual"
warm_routes = ["/", "auto"]  # optional: pre-request routes after every edit

[commands.e2e]
match = "npx playwright test"   # prefix match; a declared .sh script
                                # also matches ./script.sh and bash script.sh
requires = ["dev-server"]

You can also let your agent draft this file — it already knows the project's dev-server command, ports, and test entry points, and writing the file is an ordinary edit. Nothing executes from it until you review and approve the exact content once:

toolahead trust

Service commands run unsandboxed against the live workspace — they are the environment the agent is about to test — so a cloned repository must never start anything by merely being opened. toolahead trust records a SHA-256 of the exact file outside the repository (mode 0600); any later change to toolahead.toml revokes the approval automatically until you rerun it. Until trusted, only the safe direction applies: declared external commands are still excluded from result reuse, but no process is ever started.

To keep a service itself sandboxed, make the declared command the sandbox wrapper: command = "docker compose up dev" runs the dev server in a container with the workspace mounted read-only or read-write as you choose — isolation comes from the container, and ready.port works unchanged.

With a trusted config:

  • Services with prewarm = "mutation" start right after the first successful edit — typically while the model is still reasoning about its next step — so they are warm when the test or e2e call arrives. "start" launches them with the daemon, "manual" only on demand.

  • A command matching a [commands.*] entry is never run ahead and never served from cache. Before it executes, ToolAhead waits — bounded by the declared timeouts — until every required service passes its readiness check: through the ToolAhead run tool, and equally for the agent's native shell via the PreToolUse hooks, which deny with an actionable reason instead of allowing a doomed run when a trusted config's service stays down. Everything else stays fail-open.

  • warm_routes goes one step further: after every edit, ToolAhead GETs the listed routes as soon as the service is ready. Dev servers compile routes on demand, so the request itself absorbs the rebuild — by the time the agent's browser or e2e check arrives, the page is already compiled. The "auto" entry derives the route from the edited file for Next.js (app and pages router), Nuxt, and SvelteKit — editing app/dashboard/page.tsx warms /dashboard. On top of the heuristic, ToolAhead learns which URLs your agent actually fetches after editing a file and warms those on the next edit too. Learning requires a real fetch — an HTTP client such as curl or wget, either directly or inside a shell script the command executed. A URL that merely appears in output, a comment, or a file the agent only read is never learned, so it can never become an unexpected request later. What is learned lives in memory for the session only and is never written to disk: a cloned repository cannot ship a file that steers these requests. Warm requests are GET-only against the declared service origin, never follow redirects elsewhere, are never cached, and never overlap — a newer edit waits for the in-flight round and then supersedes it. toolahead trust prints the exact auto-GET targets before you approve them.

  • Readiness means reachable, not "has processed your latest edit": a hot-reload server that was already running may briefly still serve the previous build. ToolAhead never adds a wait for this — instead, when a run starts within seconds of an edit against an already-running service, it appends a short note to the output so the agent re-runs once instead of concluding its change had no effect. For a strict freshness barrier, use ready.command with a project-specific check (for example comparing a build ID endpoint against the sources).

  • Browser-based checks follow the same rule: ToolAhead warms the browser and the server, but a screenshot or page snapshot is always captured fresh — rendered output is not a pure function of the files.

Everything here is strictly opt-in: without toolahead.toml nothing starts and nothing changes. TOOLAHEAD_ENSURE_WAIT caps how long a hook waits for readiness (110 seconds by default, below the 120-second hook process timeout); 0 disables the wait entirely. A service whose timeout exceeds that budget cannot be fully guaranteed — raise both values together if you have one. Service output is logged to .toolahead/services/<name>.log, and toolahead status shows each service's state.

Latency metrics

toolahead status separates the parts that can otherwise be confused:

Metric

Meaning

Agent wait

Time from the previous result until the agent asks for its next tool; includes API, network, model, and reasoning time

Prefetch lead

How long ToolAhead had already been running the call before the agent asked for it

Replay wait

How much longer the prepared call still needed when the agent requested it

Tool wait removed

Native tool runtime minus actual replay/tool phase

End-to-end

Total time for the complete task; includes variable agent and API time

Acceptance

Prepared calls that exactly matched and were returned

Delivery

Prepared command results the agent actually requested and used

This is why removing 5 seconds of tool waiting does not guarantee the complete task finishes exactly 5 seconds sooner: model and API response times vary independently.

Security model

WARNING

A disposable workspace copy is not a security sandbox. Allow only commands you already trust. A malicious command can still access the network or write to absolute paths outside the copy.

  • Tool paths are contained inside the configured workspace; symlink escapes are rejected.

  • Every command run ahead uses a fresh disposable copy, never the live checkout.

  • The local daemon binds to 127.0.0.1 and adds no remote telemetry. Requests carry the workspace they came from, so a daemon serving another project refuses them instead of answering for the wrong checkout.

  • What ToolAhead has learned about a project is stored outside it, under ~/.toolahead/. Nothing a repository ships can steer what gets executed or requested.

  • Declared services never start from an untrusted toolahead.toml: approval is an explicit toolahead trust of the exact file content, stored outside the repository and revoked automatically by any change to the file.

  • Before returning a prepared result, ToolAhead hashes the current files again. Filesystem watchers only help it skip unnecessary hashing.

  • Tests that depend on external services, databases, clocks, random values, or environment state cannot be validated from source files alone. Declare them under [commands] in toolahead.toml: they are then excluded from result reuse and only their prerequisites are pre-warmed.

Limitations

  • Edit and Write are intentionally not run ahead. After either finishes, ToolAhead starts the next predicted safe tool. Rapid changes are grouped, and commands running against an older file state are stopped.

  • Prepared command results are limited to explicitly approved tests, builds, and linters whose output should be repeatable.

  • Commands currently verify the entire workspace, which can be conservative on very large monorepos. Checking only relevant dependencies is planned.

  • API and model response times can outweigh the saved tool time. Compare multiple runs with and without ToolAhead instead of relying on one attempt.

  • Hosted tools such as provider-side web search cannot be run ahead by this local integration.

  • Service readiness proves reachability, not that a hot-reload server has finished rebuilding the newest edit. ToolAhead flags this window with a note on the run output rather than adding latency; ready.command can implement a strict project-specific freshness check.

  • Windows has not yet been validated.

Development

Build and verify the PyPI artifacts:

uv build
python3 .github/scripts/normalize_sdist.py dist/*.tar.gz
python3 .github/scripts/check_distribution.py dist/*.whl dist/*.tar.gz
uvx --from twine twine check dist/toolahead-0.2.0a2*
uvx --from dist/toolahead-0.2.0a2-py3-none-any.whl toolahead --help

Project map

  • src/toolahead/ — installable CLI, MCP server, prediction engine, hooks, sandbox execution, replay, and telemetry

  • docs/assets/ — the logo and README recordings

  • .github/workflows/ — package validation and trusted PyPI publishing

  • .github/scripts/ — release-archive privacy and metadata checks

Research foundations

ToolAhead is an independent implementation informed by research on speculative tool execution. It is not an official implementation or reproduction of any single paper. The closest foundations are:

ToolAhead combines these directions with local Codex and Claude Code hooks, Antigravity workspace MCP discovery, exact call-and-workspace matching, MCP result replay, mutation generations, and a standalone Python package. All benchmark numbers above are ToolAhead's own measurements, not results reported by those papers.

License

MIT License. See LICENSE.

Contributions are welcome; see CONTRIBUTING.md. Security reports should follow SECURITY.md.

Available Tools

6 tools
edit_fileEdit file (ToolAhead)A

Replace an exact string in a workspace text file. Use this ToolAhead edit after ToolAhead read_file; it follows the familiar file_path, old_string, new_string, and replace_all contract. Edits execute normally and are never prefetched, cached, or replayed.

ParametersJSON Schema
NameRequiredDescriptionDefault
file_pathYesAbsolute path or path relative to the workspace.
new_stringYesReplacement text.
old_stringYesExact text to replace; include context when it is not unique.
replace_allNoReplace every occurrence (default false).

TDQS

A4.2/5.0
Behavior4/5

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

The description adds that edits 'execute normally and are never prefetched, cached, or replayed,' which is behavioral context beyond the annotations. The annotations already indicate non-read-only and non-idempotent, and the description is consistent, not contradictory.

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 two concise sentences with the core action upfront. Every clause adds value—purpose, usage sequence, and execution semantics—with no redundancy.

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 full schema coverage, annotations, and no output schema, the description covers the essential how and when to use, plus execution behavior. It doesn't detail error cases or return values, but those are not strictly required for a simple edit tool.

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 description coverage is 100%, so the schema already documents all parameters. The description only names the parameters via 'familiar ... contract' without adding semantic details beyond the schema, hence a baseline score.

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 explicitly states 'Replace an exact string in a workspace text file,' which is a specific verb and resource. It also names the contract parameters and positions itself relative to read_file, distinguishing it from sibling tools like read_file and write_file.

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 to 'Use this ToolAhead edit after ToolAhead read_file,' providing clear usage context. However, it doesn't explicitly state when not to use it or contrast with alternatives like write_file, so it falls short of full exclusions.

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

list_filesList files (ToolAhead)A
Read-onlyIdempotent

Find workspace files whose project-relative paths match a glob pattern. Use this instead of another glob/file-listing tool when available: ToolAhead may return an exact prefetched result.

ParametersJSON Schema
NameRequiredDescriptionDefault
pathNoDirectory to search; defaults to the workspace.
limitNoMaximum paths to return (default 200).
patternYesGlob pattern such as **/*.py or src/**/test_*.py.

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the main burden is on the description to add extra context. It adds a meaningful behavioral caveat: the tool 'may return an exact prefetched result,' which informs the agent about potential cached behavior beyond the standard 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 two concise sentences: the first states the core purpose, the second adds usage guidance and a behavioral caveat. Every word earns its place, with no filler or redundancy.

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 well-annotated schema, and the presence of safety annotations, the description is mostly complete. It covers purpose, usage, and the key prefetch behavior. A minor gap is not elaborating on what 'exact prefetched result' implies (e.g., freshness or staleness), but overall it is sufficient.

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?

The schema already describes 100% of parameters with examples and defaults, so the baseline is 3. The description adds value by clarifying that paths are 'project-relative', which is not fully explicit in the schema, and by establishing the workspace-scoped nature of the search.

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 uses a specific verb ('Find') and resource ('workspace files') with a precise criterion ('project-relative paths match a glob pattern'). It clearly positions the tool as a file-listing tool and distinguishes it from the sibling read/edit/write/run tools, while also noting its advantage over another glob/file-listing tool.

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?

The description explicitly advises using this tool 'instead of another glob/file-listing tool when available' and gives a rationale ('ToolAhead may return an exact prefetched result'). It provides clear usage context but does not name the alternative sibling (e.g., search) or specify when not to use it.

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

read_fileRead file (ToolAhead)A
Read-onlyIdempotent

Read a UTF-8 text file from the workspace with 1-based line numbers. Use this instead of another file-read tool when available: ToolAhead may return an exact prefetched result. Supports offset and limit for large files.

ParametersJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of lines to return (default 2000).
offsetNo1-based line number to start reading from.
file_pathYesAbsolute path or path relative to the workspace.

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare readOnly, idempotent, and non-destructive hints, so the safety profile is covered. The description adds useful context about UTF-8 encoding, line numbering, and offset/limit support, but doesn't disclose return format or error behavior, which would be valuable given no output 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 two sentences, front-loaded with the primary action and key details. Every sentence adds value: what it reads, the line numbering, the ToolAhead preference, and offset/limit support. No wasted words.

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

Completeness3/5

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

The tool has three parameters and no output schema, so the description must explain not only parameters but also return behavior. It covers encoding, line numbers, and pagination, but omits what exactly is returned (e.g., an array of lines with numbers vs. a string), which is a notable gap.

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?

The input schema already describes all three parameters with 100% coverage, including defaults and semantics for offset. The description adds minor context about offset/limit being useful for large files, but this is not essential given the schema's completeness.

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 reads a UTF-8 text file from the workspace with 1-based line numbers, which is specific and distinguishes it from sibling tools like list_files and search. However, it references 'another file-read tool' without naming it, so it doesn't explicitly differentiate from all possible siblings.

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?

The description gives a clear usage context: prefer this tool when available due to potential prefetched results from ToolAhead, and mentions offset/limit for large files. It also implies UTF-8 only, but doesn't explicitly state when not to use it (e.g., for binary files or other types of operations).

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

runRun replayable command (ToolAhead)A
Idempotent

Run an exact deterministic test, build, or lint command that is listed in .prefetch-replay.json. ToolAhead may replay an identical prefetched result. Commands declared under [commands] in toolahead.toml also run here: their required services are started and health-checked first (never replayed). Use the agent's native shell for commands that are neither.

ParametersJSON Schema
NameRequiredDescriptionDefault
commandYesExact command listed in .prefetch-replay.json.
descriptionNoShort description of what the command does.

TDQS

A4.6/5.0
Behavior4/5

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

Beyond the idempotentHint annotation, the description discloses that ToolAhead may replay a prefetched result and that toolahead.toml commands get their services started and health-checked instead of being replayed. It does not contradict the annotations; a small gap remains around return output/error behavior, but the key behavioral traits are covered.

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 short sentences, front-loaded with the primary purpose, followed by the replay nuance and an explicit fallback. No filler words; every clause contributes.

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?

For a tool with no output schema and a moderate annotation set, the description covers main use cases, replay behavior, and alternatives. It does not describe the return format or failure modes, but for a command runner the core behavior is sufficiently specified.

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?

The schema already covers both parameters, so baseline is 3. The description adds meaning by explaining that the command must be exact/deterministic and by expanding accepted commands to include those declared in toolahead.toml, which is not captured in the schema's command description. It doesn't fully specify how toolahead.toml commands are formatted, but enough is added.

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 specific verb and resource: 'Run an exact deterministic test, build, or lint command that is listed in .prefetch-replay.json.' It clearly distinguishes this from sibling file tools by limiting to replayable commands and directing other commands to the native shell.

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

Usage Guidelines5/5

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

It gives explicit when-to-use guidance: commands in .prefetch-replay.json or [commands] in toolahead.toml; it also states when not to use it: 'Use the agent's native shell for commands that are neither.' This is a clear exclusion rule.

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

write_fileWrite file (ToolAhead)A

Create or overwrite a UTF-8 workspace file using the familiar file_path and content contract. Writes execute normally and are never prefetched, cached, or replayed.

ParametersJSON Schema
NameRequiredDescriptionDefault
contentYesComplete UTF-8 file contents.
file_pathYesAbsolute path or path relative to the workspace.

TDQS

A4/5.0
Behavior4/5

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

The description adds behavioral context beyond the annotations by specifying UTF-8 encoding, the fact that writes execute normally, and that they are 'never prefetched, cached, or replayed.' This is useful for an AI agent to understand execution semantics. It also discloses the overwrite behavior. There is no contradiction with the annotations; readOnlyHint=false aligns with the write nature.

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 two sentences with no redundancy. The first sentence fronts the primary action and object, and the second adds essential execution context. Every word contributes to clarity, making it an excellent example of concise documentation.

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?

For a simple write tool with two parameters and no output schema, the description covers the core aspects: purpose, encoding, overwrite semantics, and execution guarantees. It does not mention return values or error handling, but these are minor for a write operation. The lack of an output schema means some gap exists, but overall it is sufficiently complete.

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?

The input schema provides 100% coverage of both parameters with clear descriptions ('Complete UTF-8 file contents' and 'Absolute path or path relative to the workspace'). The description's phrase 'familiar file_path and content contract' adds no significant semantic detail beyond what the schema already conveys, hence the baseline score of 3.

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's purpose with a specific verb and resource: 'Create or overwrite a UTF-8 workspace file'. This distinguishes it from siblings like read_file, list_files, search, and run, though edit_file is not explicitly differentiated. The mention of the 'file_path and content contract' adds scope clarity.

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 scenarios through 'Create or overwrite' and 'familiar file_path and content contract', suggesting it is for writing full file contents. However, it does not explicitly state when to prefer this tool over edit_file or other alternatives, nor does it provide exclusions or when-not-to-use guidance.

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

A4.1/5.0
Disambiguation5/5

Each tool targets a distinct file operation: reading, listing, searching, editing, writing, and running commands. There is no overlap or ambiguity among them.

Naming Consistency4/5

Most tools follow verb_noun pattern (read_file, list_files, edit_file, write_file), but search and run are single verbs. The convention is mostly consistent and readable.

Tool Count5/5

Six tools is a well-scoped set for a workspace file server with prefetching capabilities. Each tool serves a clear purpose without bloat.

Completeness4/5

The core file lifecycle (read, write, edit, list, search) is covered, plus command execution. A delete_file operation is missing, which agents may need, but it is a minor gap.

Maintenance

ActivityMaintained
ResponsivenessSyncing

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