Daimonos is an agent-optimized MCP server that replaces verbose terminal output with compact, structured JSON to reduce token usage and API costs for AI coding agents.
File Operations
read_file — Read files with optional line offset/limit; returns {unchanged:true} if file hasn't changed since last read (deduplication)
write_file — Write files with automatic parent directory creation
edit_file — Apply string-replacement edits with diff confirmation
ls — List directory contents (skips .git, node_modules, target); optional stat mode for permissions/mtime
Search
search — Regex content search or trigram-based file-name search, with optional glob filtering, directory scoping, and result limits
Command Execution
exec — Run arbitrary shell commands with semantic output filtering; returns compact {exit, out, err} instead of raw terminal output
execute_script — Run Starlark (Python-subset) scripts with all tools as built-ins; intermediate results stay sandboxed, only the final result is returned — much cheaper than sequential calls
batch — Execute multiple tool calls in a single round-trip (ideal for 2+ independent reads/searches)
Native Tool Plugins (structured JSON output)
git — status, log, diff, branch, add, commit, push, pull, checkout
cargo — test, build, check, clippy, fmt, add
gh — pr_view, pr_list, pr_create, pr_diff, pr_checks, api
docker — ps, logs, exec, images, inspect, stop, compose_up/down/ps
discord — Read-only: list_guilds, list_channels, read_messages, search_messages
Workspace & Session Management
workspace_info — Detailed workspace info: session state, root listing, index stats
snapshot — Create, restore, list, or delete workspace checkpoints
set_cwd — Change working directory for all subsequent operations
Analytics & Introspection
session_stats — Token analytics scoped to current session, cross-session history, or daily trends
set_external_session_id — Attach an external session ID to analytics rows for correlation
get_tool_schema — Retrieve the full input schema for any tool(s) on demand
list_all_tools — Reveal all available tools including extended ones (diff, pipelines, repair)
Enables Discord integration with bot token authentication, allowlists, and read-only tool support.
Provides structured tools for Docker operations including container management (ps, logs, exec, images, inspect, stop) and Compose orchestration.
Provides structured JSON output for git operations such as status, log, diff, branch, add, commit, push, pull, and checkout.
Allows interacting with GitHub repositories via the gh CLI, enabling pull request viewing, listing, creation, diff, checks, and API calls.
An agent-optimized OS layer that makes AI coding agents faster and cheaper.
Daimonos replaces the built-in file, search, exec, and git tools in your AI coding agent with structured equivalents that return compact JSON instead of raw terminal output. The result: fewer tokens consumed, fewer round-trips, and lower API costs — with zero changes to your workflow.
Platforms: Linux (x86_64, aarch64) and macOS (Apple Silicon, Intel). Windows support is planned.
For repository agent/operator conventions, see AGENTS.md (especially
Daimonos tool usage policy).
The name comes from Greek daimon (agent/spirit), the etymological root of "daemon."
When an AI agent runs cargo test, it gets back hundreds of lines of terminal
output — progress bars, compile messages, passing test names — when all it
needs is "47 passed, 0 failed." The agent pays for every token of that noise:
reading it, reasoning about it, and carrying it in context for the rest of the
session.
The same waste happens with git status, docker ps, ls -la, and every
other shell command. Agents spend 30-50% of their token budget on verbose,
unstructured tool output.
Related MCP server: token-pilot
Daimonos runs as an MCP server that your IDE or CLI spawns automatically. It provides the same operations agents already use — read files, write files, search, execute commands, git operations — but returns compact, structured JSON instead of raw text.
The single binary also provides ACP, one-shot agent, interactive chat, and socket-daemon runtimes — a full coding-agent harness in its own right; see Agent harness features below and Runtime modes for the explicit subcommands and compatibility aliases.
Agent: exec("cargo test")
Without Daimonos (raw terminal output):
Compiling inventory v0.1.0 (/workspace)
Finished `test` profile [unoptimized + debuginfo] target(s) in 2.31s
Running unittests src/main.rs (target/debug/deps/inventory-abc123)
running 47 tests
test config::tests::test_default ... ok
test config::tests::test_load ... ok
... (200+ more lines)
test result: ok. 47 passed; 0 failed; 0 ignored
With Daimonos (structured JSON):
{"ok":true,"tests":47,"passed":47,"failed":0,"failures":[]}Native tool plugins — git, cargo, gh, and docker are exposed as
first-class MCP tools with structured JSON output. When agents call
exec("cargo test"), Daimonos intercepts it and routes through the native
plugin instead.
Semantic output filters — For commands without native plugins (pytest, make, pip install, eslint, etc.), Daimonos applies semantic compression: test runners return summary + failures only, build commands return "ok" or just the errors, install commands return success/failure.
Protocol-level efficiency — Read deduplication (re-reading an unchanged
file returns {"unchanged":true} instead of the full content), compact
field names, lazy tool exposure, batch operations, and a terse output
directive that cuts LLM prose by ~30%.
Managed subprocess execution — Command output is bounded while it is read instead of after full buffering. Daimonos owns Unix process groups, retires descendants on cancellation or session shutdown, isolates child environments through an explicit allowlist, and stores background output in private bounded artifacts.
Tested with Claude Opus 4.6 on identical coding tasks (read files, search code, edit, run tests, git operations):
Metric | Baseline | Daimonos | Savings |
Output tokens | 5,842 | 3,198 | -45.3% |
Total tokens | 41,239 | 33,847 | -17.9% |
Tool calls | 17 avg | 14 avg | -17.6% |
Wall time | 42.1s avg | 35.2s avg | -16.4% |
Remote benchmarks on AWS (same hardware, same model, same tasks) showed 20.3% cost reduction and 14.0% faster task completion.
Five instances from swe-bench-verified-mini,
same model (Claude Opus 4.8) across all three harnesses, each agent running
inside the official SWE-bench Docker image for its instance (real test
environment), scored with the official swebench evaluation harness:
Instance | daimonos tokens (LLM calls) | mini-swe-agent tokens (LLM calls) | cursor-agent tokens |
django__django-11815 | 54,755 (4) | 38,305 (9) | 113,242 |
django__django-12155 | 53,773 (4) | 33,095 (9) | 348,425 |
django__django-12708 | 88,765 (6) | 196,492 (22) | 451,349 |
sphinx-doc__sphinx-8035 | 172,729 (10) | 342,108 (30) | 930,495 |
sphinx-doc__sphinx-9367 | 66,812 (5) | 18,611 (6) | 230,223 |
Total tokens | 436,834 | 628,611 | 2,073,734 |
Total wall time | 84 s | 247 s | 279 s |
Resolved | 5/5 | 5/5 | 5/5 |
Conclusions:
Correctness parity: all three harnesses resolved 5/5 at this sample size, so daimonos's token savings did not cost any resolutions.
daimonos was cheapest and fastest: ~30% fewer tokens than mini-swe-agent (the minimal open-source baseline) and ~4.8x fewer than cursor-agent, with ~3x less wall time than either.
Caveats: n=5, and run-to-run variance is real (daimonos spent 503k tokens on sphinx-8035 in an earlier identical-config run vs 173k here). cursor-agent's total is mostly cache-read tokens billed at a fraction of input price, so its raw token count overstates its relative cost. Measured API spend for the batch (OpenRouter): daimonos $1.53, mini-swe-agent $1.66; cursor-agent bills via Cursor's backend.
See benchmarks/swebench/ for the runners and methodology.
Use this script for README readers, release notes, and social posts:
# 1) Install daimonos
cargo build --release
sudo cp target/release/daimonos /usr/local/bin/
# 2) Configure your MCP client (example: Cursor)
# .cursor/mcp.json -> command: daimonos, args: ["--mcp", "-w", "/path/to/project"]
# 3) Ask your agent to run:
# "Run cargo test and summarize failures only."
# "Show git status as structured output."What to highlight in the demo:
same workflows, less tool-output noise
structured responses instead of raw terminal spam
fewer tokens and fewer round-trips for common coding tasks
Pre-built binaries (Linux and macOS):
# Linux x86_64
curl -L https://github.com/beardfaceguy/daimonos/releases/latest/download/daimonos-x86_64-linux.tar.gz | tar xz
sudo mv daimonos /usr/local/bin/
# macOS Apple Silicon
curl -L https://github.com/beardfaceguy/daimonos/releases/latest/download/daimonos-aarch64-macos.tar.gz | tar xz
sudo mv daimonos /usr/local/bin/From source:
git clone https://github.com/beardfaceguy/daimonos.git
cd daimonos
cargo build --release
sudo cp target/release/daimonos /usr/local/bin/See docs/install.md for all platforms (ARM Linux, Intel Mac, musl static builds).
For most users, start with one of these:
Cursor: Cursor IDE setup
Zed: Zed setup
Claude Code: Claude Code setup
Add Daimonos as an MCP server. For Cursor, add to your project's
.cursor/mcp.json:
{
"mcpServers": {
"daimonos": {
"command": "daimonos",
"args": ["--mcp", "-w", "/path/to/your/project"]
}
}
}That's it. Daimonos starts when your IDE opens the project and exits when you close it. No daemon to manage, no background service.
GitHub Copilot (VS Code, Visual Studio, JetBrains, Xcode, Eclipse)
Claude Code (CLI + macOS Desktop app)
Cline (VS Code extension)
Discord integration (bot token, allowlists, read-only tools)
Other tools (Claude Desktop, ChatGPT, Continue.dev, BoltAI, etc.)
Tool | What it does |
| Read with optional offset/limit, content-hash deduplication |
| Write with auto-mkdir |
| String replacement with diff confirmation |
| Regex search (content mode) or file discovery (file mode) |
| Run commands with semantic filtering, bounded capture, and owned teardown |
| Multiple operations in a single round-trip |
| Project type, git status, directory listing, analytics |
These appear automatically when the corresponding CLI tool is found on PATH:
Plugin | Commands | Detected by |
| status, log, diff, branch, add, commit, push, pull, checkout |
|
| test, build, check, clippy, fmt, add |
|
| pr_view, pr_list, pr_create, pr_diff, pr_checks, api |
|
| ps, logs, exec, images, inspect, stop, compose_up/down/ps |
|
Workspace snapshots — Checkpoint before risky edits, rollback on failure
Starlark scripting — Bundle multiple tool calls into a single script
Token analytics — Per-tool-call tracking with cross-session history (daimonos --stats)
Background processes — Start, poll, and stop long-running commands with admission limits, private bounded logs, and descendant cleanup
Configurable — All tunables in a single TOML config file
Raw exec, background jobs, and CLI plugins (cargo, git, gh, docker,
npm, pytest, curl, and shellcheck) share one managed execution layer:
Streaming-time bounds — stdout and stderr retain UTF-8-safe head/tail previews without first allocating the complete output
Process-group ownership on Unix — cancellation and shutdown send TERM, wait a configurable grace period, then escalate to KILL and reap descendants
Secure background artifacts — random exclusive 0600 files under a
private 0700 directory, with configurable byte and job-count limits
Environment isolation — children inherit only configured parent variables plus explicit session, tool, and per-call overrides; provider and MCP credentials are not ambiently leaked
Structured-output integrity — plugins reject truncated JSON rather than reporting an incomplete result as valid
Beyond the MCP server, the same binary is a complete coding-agent harness:
an interactive terminal UI (daimonos agent), an ACP backend for Zed, a
one-shot CLI, and a session daemon with attach/detach and remote control.
Many of its recent features come from a systematic study of 60+ open-source agent harnesses (Aider, OpenHands, SWE-agent, Goose, OpenCode, Forge, Pi, the Cline family, and others) — mining the ecosystem for proven techniques and adapting the best ones.
Bounded provider retries with backoff for transient failures (429/5xx/ network), classified at the provider boundary — fatal auth/validation errors surface immediately
Automatic model failover — on a sustained overload the turn continues on the next model in the chain, then returns to your preferred model on the next turn
Turn-level error resume — when retries and failover are spent, the agent pauses, repairs the conversation (keeping partial streamed output), and continues where it left off; recovery actions surface in the UI
Retry-storm detection — fingerprints repeated identical tool calls and steers the model out of loops
Orphan tool-call repair — max-token truncation mid-tool-call is repaired instead of poisoning the session
Several providers, one session — configure Anthropic, OpenAI, and
OpenRouter side by side (DAIMONOS_AGENT_<NAME>_API_KEY); every call is
routed to the right provider by model, with an explicit provider:slug
override
Live model discovery — at startup the configured provider(s) are queried for their full model catalogs; the model picker and failover chain always reflect what is actually served, newest first
Cross-provider failover — with more than one provider configured, an outage at one can fail over to models at another, mid-turn
Provider-reported context windows — compaction thresholds derive from the live model metadata instead of hardcoded numbers
Conversation compaction — summarize-and-continue with high/low water-mark thresholds and provider-honest token accounting
Bounded tool results — oversized tool output is capped at the dispatch boundary and offloaded to files the agent can re-read selectively
Reverse-budget pruning — old tool results shrink before new ones, so a long turn keeps its recent working set sharp
Distilled working memory — durable facts/snippets/notes that survive compaction, separate from the transcript
Resilient edit matching (mined from Aider) — whitespace-tolerant search/replace cuts failed-edit retry costs
Batched scripting — the agent is steered to bundle multi-step tool work into single Starlark scripts (~2.2x cost lever, benchmarked)
Per-turn workspace checkpoints — automatic snapshots with diff/compare and code-only restore
Daemon-owned sessions — detach from a running agent, reattach later (or from another terminal), with a reconnect event ring and canonical snapshot recovery
Persistent terminal UI — streaming output, tool-lifecycle cards, approval modal, model/usage status bar, and vim-style scrollback
Remote control — paired Android controller over an authenticated WSS gateway
Subagent delegation — drive external ACP agents (cursor-agent, codex-acp, …) as delegated workers
Thought capture — opt-in local persistence of streamed model reasoning for later inspection
Agent-mode configuration lives in a dotenv-style agent.env
(~/.config/daimonos/agent.env); see Runtime modes.
Daimonos is a single Rust binary with two planes that share one tool implementation, one opcode protocol, one config, and one analytics store:
Tool server — speaks MCP over stdio (or a Unix socket) to an external agent. Your IDE spawns it as a subprocess — no network, no containers, no setup beyond a JSON config entry.
Agent harness — runs the agent loop itself, dispatching those same tools in-process (no MCP hop) and talking to LLM providers directly.
┌──────────────┐ MCP (JSON-RPC over stdio) ┌─────────────────┐
│ AI Agent │ ◄──────────────────────────────► │ Daimonos │
│ (Cursor, │ │ │
│ Copilot, │ Structured JSON responses │ ┌───────────┐ │
│ Claude, │ ◄──────────────────────────────── │ │ File ops │ │
│ etc.) │ │ │ Search │ │
│ │ │ │ Exec │ │
│ │ │ │ Git │ │
│ │ │ │ Cargo │ │
│ │ │ │ Docker │ │
│ │ │ │ GitHub │ │
│ │ │ │ Snapshots │ │
│ │ │ │ Analytics │ │
└──────────────┘ │ └───────────┘ │
└─────────────────┘Under the hood, Daimonos uses an opcode-based protocol where each operation
has a numeric identifier and compact field names (c, p, s, n) to
minimize token overhead. The MCP layer translates between standard JSON-RPC
and the internal opcode format.
┌────────────────────────────────────────────────────────┐
│ Frontends: TUI · ACP (Zed) · one-shot CLI · chat REPL │
│ session daemon (attach/detach, Android) │
├────────────────────────────────────────────────────────┤
│ Shared session core: agent loop · canonical events │
│ compaction · tool-result bounding · working memory │
│ checkpoints · approvals/safety policy │
├────────────────────────────────────────────────────────┤
│ Provider layer: retries · model failover · resume │
│ multi-provider router (routes each call by model) │
│ ├─ Anthropic adapter │
│ ├─ OpenAI adapter │
│ └─ OpenRouter adapter │
└────────────────────────────────────────────────────────┘Every frontend drives the same transport-independent session core, so a
conversation started in the TUI can detach to the daemon and be reattached
from another terminal or a paired phone. Provider adapters own all
provider-specific wire format and error classification; everything above
them sees one LlmProvider interface and plain model strings — which is
what makes failover, live model discovery, and multi-provider routing
composable rather than special-cased.
Daimonos is being built in three phases:
A Rust binary that runs on any Linux or macOS machine, in two roles that prove out the same protocol design and structured I/O patterns:
Tool server for third-party agents (Cursor, Copilot, Claude Code, Zed, …) via MCP — the original phase-1 deliverable.
Agent harness in its own right: interactive TUI, ACP backend for Zed, one-shot CLI, and daemon-owned sessions with remote attach — with multi-provider routing, model failover, compaction, and per-turn checkpoints built in (see Agent harness features).
Status: Production-ready. Both roles are used daily for real development work — including developing Daimonos itself. Pre-built binaries available for Linux (x86_64, aarch64, musl) and macOS (Apple Silicon, Intel).
A purpose-built Buildroot Linux image with Daimonos as the primary user-space application. Designed for cloud deployment where AI agents need a clean, minimal environment. The distro boots in seconds, has no shell or human-facing UI, and runs the Daimonos daemon as PID 1's direct child. The session daemon and remote-control gateway from phase 1 are the intended tenants: headless agent sessions in the cloud, attached to from a terminal or phone.
Status: Working prototype. Boots in QEMU, deployable to AWS EC2. Used for remote benchmarking.
The long-term vision: a microkernel where Daimonos opcodes become native syscalls. StructFS (a filesystem that stores and returns structured data natively), capability-based security, and a process model designed for agent workloads from the ground up.
Status: Design phase.
Dependency | Required | Install |
Rust (stable 1.75+) | Build | |
Python 3 + pytest | Tests |
|
# Rust unit tests (350+ tests, parallel-safe)
cargo test
# End-to-end MCP protocol tests (150+ pytest cases)
python3 -m pytest tests/ -vcd benchmarks
./setup-mcp.sh
./run-benchmark.sh baseline # IDE built-in tools
./run-benchmark.sh daimonos # routed through daimonos MCP
python3 analyze-results.py results/See benchmarks/README.md for details.
All behavior is tunable via a TOML config file. See docs/configuration.md for the full reference, or daimonos.default.toml for annotated defaults.
Key sections:
[index] — Trigram indexer tuning (max depth, file size limits)
[search] — Search result limits
[process] — Process timeouts, in-memory/artifact bounds, background
admission, termination grace, inherited environment, semantic filters, and
max concurrent Starlark script threads
[pipeline_cache] — Subprocess result cache size, inotify watch cap, extra ignored directories
[analytics] — Token tracking (SQLite storage, retention)
[tools.*] — Per-tool plugin configuration
Daimonos is in active development. If you're interested in contributing, start with the AGENTS.md file for coding conventions, architecture decisions, and the review checklist.
See also:
MIT
Multiple tools in one call. Always batch when you need 2+ independent reads/searches. E.g. [{tool:"read_file",arguments:{path:"a.rs"}},{tool:"search",arguments:{pattern:"TODO"}}].
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It discloses that the tool executes multiple independent reads/searches and gives an example, but lacks details on limits, error handling, ordering, or support for write operations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences plus a code example, no wasted words. Highly efficient and front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a meta-tool with no annotations, no output schema, and no parameters, the description is adequate. It explains purpose and usage. Could mention if writes are allowed or error behavior, but overall complete enough.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has no parameters (100% coverage), so baseline is 3. The description adds value by providing a concrete example showing how to structure batch calls, which clarifies usage beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states 'Multiple tools in one call' and specifies it is for batching independent reads/searches. It distinguishes itself from sibling tools which are individual tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Always batch when you need 2+ independent reads/searches', providing clear context. Does not mention exclusions or alternatives, but the guidance is strong.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Cargo operations. Commands: test, build, check, clippy, fmt, add.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. Description does not disclose side effects, safety profile, or return behavior. The agent cannot infer what happens when the tool is invoked.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Very concise at two sentences, but the first sentence 'Cargo operations' is vague and not front-loaded with the most critical information. Still, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite the simple input schema, the description is woefully incomplete. It does not clarify the tool's function, invocation method, or output. Agent cannot reliably use this tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema is empty (0 parameters). Description adds context that the tool deals with cargo commands, but does not explain how commands are specified. With no parameters, the description's value is limited.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description says 'Cargo operations' which is vague, and lists commands without clarifying whether the tool executes them or just lists them. It barely distinguishes from sibling tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like exec or execute_script. No context on prerequisites or suitable scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Discord read-only operations. Commands: list_guilds, list_channels, read_messages, search_messages.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description bears full responsibility for behavioral disclosure. It states the tool is read-only, which is important, but it does not mention any other behavioral traits such as rate limits, authentication requirements, output format, or potential side effects. For a tool that groups multiple sub-commands, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise, consisting of a single sentence. However, it lacks structure; listing commands inline without bullet points or separators makes it less readable. It could be more efficient by grouping related info, but it does not contain unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that encapsulates multiple commands (list_guilds, list_channels, read_messages, search_messages), the description is too brief. It does not explain what each command does, what inputs they require, or what outputs they produce. Since there is no output schema, the description should provide more context to help the agent understand usage.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, so schema coverage is 100%. The description adds no parameter-related information, but none is needed. The baseline of 3 is appropriate for complete schema coverage, and the absence of parameters means the description does not need to compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it is for Discord read-only operations and lists four specific commands. This distinguishes it from writable tools and provides a clear resource scope. However, it does not describe the functionality of each command, which slightly reduces clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies that this tool should be used for read-only Discord interactions, but it provides no guidance on when to use it versus alternatives, nor does it mention any prerequisites or context-specific usage. There are no sibling tools dedicated to Discord, so the usage context is implicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Docker operations. Commands: ps, logs, exec, images, inspect, stop, compose_up, compose_down, compose_ps.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description must disclose behaviors. The description lists commands such as 'stop' (destructive) but does not explain side effects, permissions needed, or whether operations are safe. The tool's behavior is opaque, and the lack of parameters makes invocation unclear.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short but poorly structured. The phrase 'Docker operations' is redundant with the tool name, and the comma-separated command list lacks hierarchy or explanation. Critical information is missing, making the text underspecified rather than concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of Docker operations (multiple sub-commands), the empty input schema, and lack of output schema, the description is highly inadequate. An agent cannot determine how to invoke specific commands, handle errors, or interpret results. The description fails to provide necessary context for a tool with zero parameters and no annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty (no parameters), and schema coverage is 100%. Basline is 3, but the description does not explain how to specify which Docker command to run. It adds a list of commands but fails to map them to any input, leaving the agent without guidance on invocation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Docker operations' and lists commands like ps, logs, exec, etc. However, the tool name 'docker' is already broad, and the description does not clarify the tool's specific role or how it differs from a general Docker CLI. The purpose is vague and restates the name with minimal addition.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool vs alternatives. Sibling tools include 'exec' and 'batch', but there is no mention of choosing this tool over them or any prerequisites. The description lacks explicit context for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
String-replace edits. Returns {applied, diffs} confirming each change.
| Name | Required | Description | Default |
|---|---|---|---|
| edits | Yes | [old, new, old, new, ...] pairs | |
| path | Yes | Relative path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided; description only mentions mutation and return confirmation, but fails to disclose error handling, atomicity, or permissions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence plus return info is efficient, but lacks structured flow or bullet points that could improve readability.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Simple tool with two params and no annotations; description covers basic purpose and return, but misses edge cases and comparison to sibling tools.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers both parameters with descriptions; tool description adds context that edits are old/new pairs but path description remains basic. Baseline 3 maintained.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states verb (string-replace) and resource (file edits), and distinguishes from write_file by specifying in-place string replacement rather than full content rewriting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage for string replacements, but no explicit guidance on when to use this over write_file or batch, and no exclusion criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run command. Returns {exit, out, err?}. Output auto-truncated if very large.
| Name | Required | Description | Default |
|---|---|---|---|
| args | No | ||
| command | Yes | ||
| cwd | No | ||
| env | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses the return structure and auto-truncation behavior, adding value beyond the schema. However, it omits critical aspects like side effects, security implications, or destructive potential, which are essential for a command execution tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description consists of a single concise sentence plus a brief note on return format and truncation. Every word serves a purpose with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and four parameters with no descriptions, the description is insufficient. It does not cover how to use optional parameters or handle complex scenarios.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must explain parameters but only mentions 'command' implicitly. It fails to describe 'args', 'cwd', or 'env', leaving their semantics entirely to the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Run command' clearly states the action and resource, and mentions the return format and output truncation. However, it does not differentiate from sibling tools like 'execute_script' or 'batch', which could cause confusion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
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, nor does it specify prerequisites or exclusions. It only states what it does, leaving the AI to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run a Starlark (Python-subset) script with all tools as built-in functions. Intermediate results stay in the sandbox; only result variable is returned. Much cheaper than sequential tool calls.
| Name | Required | Description | Default |
|---|---|---|---|
| code | Yes | Starlark source. Set `result` variable for output. | |
| timeout | No | Max seconds (default: 60) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It discloses sandbox behavior and cost efficiency, but omits important traits like potential security risks, error handling, or permissions needed for executing arbitrary scripts.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. It front-loads the core action and immediately provides critical behavioral context, making it highly efficient for an AI agent.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite no output schema, the description explains the return behavior (only result variable). It mentions sandbox and cost efficiency. However, it could be improved by noting error behavior or limitations, but it's generally complete for an agent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so baseline is 3. The description adds value by specifying that the result variable must be set in the code, which is a key semantic nuance beyond the schema's basic description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it runs a Starlark script with all tools as built-in functions, and distinguishes from sequential calls. It specifies the language, sandbox behavior, and return value, making the purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description mentions it's 'much cheaper than sequential tool calls,' which provides a clear use case. However, it does not explicitly state when not to use it or compare to other siblings like batch, leaving some guidance gaps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get full inputSchema for tool(s). Call before using a tool whose schema was not in list_tools.
| Name | Required | Description | Default |
|---|---|---|---|
| tools | Yes | Tool name(s) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. It describes a read-only operation (fetching schema) but doesn't explicitly state non-destructiveness. Adequate but minimal.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences: first states purpose, second gives usage guidance. No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description is sufficient. It covers purpose and usage. Could mention return value but not necessary.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description 'Tool name(s)' adds no extra meaning beyond the schema. Baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves the full input schema for specified tools, and provides a usage hint distinguishing it from list_all_tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says to call before using a tool whose schema was not in list_tools, giving clear when-to-use guidance. Does not mention when not to use, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub CLI. Commands: pr_view, pr_list, pr_create, pr_diff, pr_checks, api.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, and the description fails to disclose any behavioral traits such as side effects, authentication requirements, or whether it is read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief but not adequately informative. It is concise but lacks essential structure or front-loaded key details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (no parameters yet lists commands), the description is completely inadequate. No explanation of invocation, outputs, or behavior is provided.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema is empty, so schema coverage is 100% trivially. However, the description lists commands that imply parameters are needed, creating confusion. No parameter meaning is added.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description says 'GitHub CLI' and lists subcommands but does not specify what the tool actually does or how it executes those commands. Without input schema parameters, the purpose is vague and confusing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus sibling tools like git or exec. The description offers no context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Git operations. Commands: status, log, diff, branch, add, commit, push, pull, checkout. All args besides 'command' are passed through.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'All args besides 'command' are passed through,' but the input schema has no parameters, creating a contradiction. No behavioral traits (e.g., destructive actions, authentication needs, rate limits) are disclosed, leaving the agent without critical safety information.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise with two sentences that quickly convey the purpose. It front-loads the main idea ('Git operations') and lists commands efficiently, though the pass-through statement could be clearer.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool that wraps Git commands, the description should explain how to invoke specific commands and handle arguments. The missing 'command' parameter in the schema and lack of output format details leave the tool incomplete. Without annotations, the description fails to provide a complete behavioral picture.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With schema coverage at 100% (no parameters), the baseline is 3. The description adds information about pass-through arguments, but the mismatch between the implied 'command' parameter and the empty schema reduces clarity. It hints at semantics but fails to define how to pass parameters correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Git operations' and lists common Git commands, making it clear that this tool executes Git subcommands. It distinguishes from sibling tools like cargo or gh by specifying Git. However, it lacks specificity about the exact scope beyond listing commands.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives like exec or execute_script. The description does not mention prerequisites, typical use cases, or scenarios where this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Show all available tools including extended ones (diff, pipelines, repair). Call once to unlock them.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Without annotations, the description carries the full burden. It discloses a key behavioral trait: calling this tool unlocks extended tools. This adds valuable context beyond the schema. However, it does not mention any other potential behaviors (e.g., idempotence), but the tool is simple enough.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence with no wasted words. It is front-loaded and every word adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (no parameters, no output schema, no annotations), the description is complete. It states the action and a key behavioral note, which is sufficient for an agent to use the tool correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and schema coverage is 100%. The description does not need to add parameter info. The baseline for 0 parameters is 4, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: showing all available tools, including extended ones (diff, pipelines, repair). The verb 'show' and resource 'all available tools' are specific and distinct from sibling tools that perform other actions.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit usage guidance: 'Call once to unlock them.' This tells the agent when to use the tool (once to unlock extended tools). While it doesn't mention when not to use or alternatives, the context is clear and helpful.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List directory. Returns [{n,d,s}]. Skips .git/node_modules/target. Use stat=true for permissions+mtime.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses skip patterns and return format, which is helpful. However, it mentions a 'stat=true' parameter that is not defined in the input schema, creating a contradiction that reduces trust in the tool's actual behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences and efficiently covers purpose, return format, and skip rules. The mention of an undocumented parameter slightly detracts but overall it is well-structured and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple listing tool, the return format is described, but the meaning of 'n,d,s' is not explained. The missing parameter 'stat' and lack of clarification on its intended use leave the description incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has zero parameters with 100% coverage, so baseline is 4. The description references a 'stat' parameter not in schema, which adds confusion rather than value. It does not clarify the actual (non-existent) parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists a directory and specifies return format and skip patterns. However, the mention of 'stat=true' implies a parameter not present in the schema, which slightly muddles clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use ls versus sibling tools like read_file or search. The description does not provide context for appropriate usage or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Read file. Returns {content, lines} or {unchanged:true, lines} if already read and unmodified.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max lines | |
| offset | No | Start line (0-based) | |
| path | Yes | Relative path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description reveals important caching behavior (returns {unchanged:true, lines} if already read and unmodified) and return format. With no annotations, this adds value. However, it does not disclose potential read limitations like binary files or encoding.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no fluff, front-loaded with purpose. Every sentence provides essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description covers purpose, return format, and caching behavior. However, it lacks details on file size limits or encoding. No output schema, so description compensates well.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds no extra meaning beyond the schema's param descriptions. Baseline score is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads a file, which is a specific verb-resource. It distinguishes from sibling tools like write_file and edit_file implicitly, but could be more specific about file types or encoding.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like exec or snapshot. The description lacks context on appropriate use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Regex content search or trigram file-name search.
| Name | Required | Description | Default |
|---|---|---|---|
| glob | No | e.g. *.rs | |
| max_results | No | ||
| mode | No | Default: content | |
| path | No | Scope dir | |
| pattern | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavioral traits. It only mentions 'regex content search' and 'trigram file-name search' without explaining important behaviors like recursion depth, case sensitivity, performance implications, or side effects. Key behavioral aspects are missing.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that is front-loaded with the core purpose. It is efficient with no wasted words, though it could benefit from slightly more structure to separate the two modes clearly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 5 parameters (1 required) and no output schema, the description is too brief. It fails to cover return values, edge cases, search behavior (e.g., recursive by default?), or how to effectively use the tool. The description is incomplete for the complexity of the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 60%, and the description adds minimal value beyond the schema. It clarifies that 'pattern' is used for regex or trigram depending on mode, but does not elaborate on 'max_results' or provide additional context for unspecified parameters. It meets the baseline for a tool with moderate schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool performs regex content search or trigram file-name search, specifying two distinct modes via the 'mode' parameter. However, it does not explicitly differentiate from potential sibling tools like 'ls' or 'read_file' which could also be used for searching, but given the sibling list, no similar search tool is present, so it's reasonably clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description only states what the tool does without any guidance on when to use it versus alternatives. It does not mention prerequisites, limitations, or situations where other tools might be more appropriate. There is no explicit 'when-to-use' or 'when-not-to-use' advice.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Token analytics. Scopes: session (current totals), history (cross-session), daily (trend). Optional external_session_id filters history/daily to a single agent-runtime session id.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description mentions an optional parameter 'external_session_id' that does not exist in the schema (which has no parameters). This is a contradiction between description and schema. No information about side effects, permissions, or data returned.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is brief but contains inaccurate information (referencing a non-existent parameter). Conciseness is undermined by the lack of accuracy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without output schema or annotations, the description should clearly explain return values. It does not describe what the tool outputs (e.g., format, fields). The scoping hints provide some context but not enough for complete understanding.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has zero parameters, but the description claims an optional 'external_session_id' parameter. This misleading addition degrades the agent's ability to invoke the tool correctly. Schema coverage is 100% for an empty schema, but the description adds false semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states 'Token analytics' with three scopes (session, history, daily), suggesting it returns token usage statistics. However, it does not specify what metrics are returned (e.g., count, cost, etc.), making the purpose only moderately clear.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool over siblings like set_external_session_id. No preconditions or exclusions are mentioned, leaving the agent to infer usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Change working directory for all subsequent operations. Returns {cwd, previous}.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses the return format and the stateful nature ('all subsequent operations'), which is sufficient for a simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise (two short sentences) with no filler, front-loading the action and return value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with no parameters and a simple side effect, the description fully explains what it does and what it returns, leaving no gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are no parameters, so the description adds no value beyond the schema, but the baseline for zero parameters is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Change working directory') and the resource, and specifies that it affects all subsequent operations, distinguishing it from other file-related tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It explicitly states the purpose (for all subsequent operations) without needing exclusions, but does not provide explicit 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.
Attach an agent-runtime session id (e.g. claude --session-id UUID) to every subsequent analytics row from this connection. Use to correlate daimonos analytics with the agent's own usage logs. Pass an empty string to clear.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description says 'Pass an empty string to clear' but input schema has no parameters, creating a misleading behavioral trait that contradicts the structured schema definition.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences are concise, but the structure is flawed by implying a parameter that doesn't exist, making the description misleading.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Missing critical context on how to actually provide the session ID; the inconsistency with schema leaves the description incomplete for a parameterless tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has no parameters (100% coverage), but description implies a string parameter exists, adding incorrect semantics beyond what schema defines.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description states the tool attaches a session ID to analytics rows and mentions clearing with empty string, but purpose is undermined by contradiction with input schema (no parameters defined).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implies usage for correlating analytics logs and clearing, but no explicit when-to-use or alternative guidance relative to siblings like session_stats.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Workspace snapshots. Actions: create (returns id), restore (rolls back), list, delete.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description is the sole source of behavioral info. It lists four distinct actions but provides no mechanism to specify which one, which contradicts the empty input schema. This omission leaves the agent unable to correctly invoke the tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very short (one sentence), which is concise, but it tries to pack multiple actions without structure. It lacks front-loading of the core function and could be more effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no annotations, no output schema, and an empty input schema, the description is severely incomplete. It does not explain how to specify actions, what inputs are required, or what the return values look like, leaving the agent underinformed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0 parameters (100% coverage, empty). The description implies parameters are needed (e.g., action type) but doesn't define them, creating confusion. Baseline of 4 for no params is reduced due to misleading implications.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Workspace snapshots' and lists actions (create, restore, list, delete), but fails to specify which action is performed upon tool invocation. The lack of parameters in the input schema makes the action selection ambiguous, reducing clarity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus siblings like 'search' or 'exec'. The description does not explain under what circumstances snapshot management is appropriate, nor does it mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detailed workspace info (session state, root listing, index stats). Basic info is already in server instructions — only call if you need index stats or full detail.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It lists what the tool returns (session state, root listing, index stats), but lacks details on potential side effects or performance implications. However, for a read-like tool, this is sufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core function, followed by usage guidance. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description adequately explains return values (session state, root listing, index stats). It is complete for a parameterless tool with many siblings.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has no parameters, and the description does not need to add parameter semantics. Baseline for 0 params is 4.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies it provides detailed workspace info (session state, root listing, index stats) and distinguishes it from basic info in server instructions, making the purpose clear and distinct from siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly states 'only call if you need index stats or full detail', providing clear when-to-use and when-not-to-use guidance, and references server instructions as an alternative.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Write file, creating parent dirs.
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | ||
| path | Yes | Relative path |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It only discloses parent directory creation, omitting overwriting behavior, permissions, side effects, or error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise and front-loaded. Every word provides value, no filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, no annotations, and minimal description. Missing details on return values, error cases, and behavioral nuances for a write operation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 50% (path has description). Description adds no parameter-specific meaning beyond the schema, just behavioral context. Baseline score appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Write' and the resource 'file', with additional behavior 'creating parent dirs'. It distinguishes from sibling tools like read_file and edit_file.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use vs alternatives. The context implies file creation/overwriting, but no when-not scenarios or alternative tool mentions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v0.1.0batchcargodiscorddockeredit_fileexecexecute_scriptget_tool_schemaghgitlist_all_toolslsread_filesearchsession_statsset_cwdset_external_session_idsnapshotworkspace_infowrite_fileEach tool has a clearly distinct purpose with no notable overlap. Tools like exec and execute_script serve different functions (system commands vs. Starlark scripts).
Names mix single-word commands (cargo, docker, exec) with snake_case phrases (edit_file, set_external_session_id), lacking a uniform pattern but still readable.
20 tools is slightly above the ideal 3-15 range, but justified by the broad domain coverage including Docker, Discord, and version control.
Covers file, search, git, GitHub, Docker, Discord, scripting, and session analytics well. Minor gaps like a dedicated delete_file tool, but edit_file and write_file cover most needs.
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