ramen
One line: this server gives an agent three pure, stateless utilities — exact arithmetic, unit conversion, and text counting — each callable with the group's key and returning schema-validated results.
demo_calculator_tool— apply one binary operation (add,subtract,multiply,divide) to two numbersvar1andvar2, returning the numericresult. Division by zero returns a tool error (isError) with a one-sentence message; all three arguments are required.unit_convert— convertvaluefromfrom_unittoto_unitwithin one family: length (mm, cm, m, km, inch, foot, yard, mile), mass (mg, g, kg, tonne, ounce, pound) or temperature (celsius, fahrenheit, kelvin). Units match case-insensitively; unknown units or cross-family conversions (e.g. length to mass) return an error naming the accepted units. No currencies, dates or compound units like km/h.word_count— count a text'scharacters(Unicode code points, whitespace included),words(whitespace-separated runs) andlines(newline-separated; a trailing newline adds no line). Empty text yields zeros; no tokenisation or language detection.Common properties — all three are pure and stateless with no side effects, no network calls and no auth beyond the group's key; each declares an
outputSchemathat is validated on every result.Practical limit — only single operations are supported: multi-step arithmetic or compound conversions must be chained across calls or done elsewhere.
Docs: https://bkraad47.github.io/ramen/ · Get started · The MCP repo · Connect a client with OAuth · How it works · Deploy on GCP · Deploy on AWS · Deploy on Azure · Contracts · Releases
Streamable HTTP is the front door. Every worker serves
POST /mcp— a URL and a bearer header, nothing to install — next to the gRPC service it has had since 0.3.1, on the same port, through the same guards. Phones, browsers and hosted agent platforms connect directly; the stdio bridge stays for clients that only speak stdio. Per-user access through OAuth (the console is the authorization server), live-verified on GKE since 0.5.1.
What is true today, before the pitch. Current release 0.7.5. The local stack and CI prove both transports on real node processes on Linux and Windows. 0.7.5 itself was deployed on GKE, EKS and AKS on 2026-10-10/11 and passed the same 33 checks on each: a canary deploy of the demo repo with its NeMo rails and golden cases, custom roles and per-tool access, a person's token over Streamable HTTP and through the stdio bridge, and the guardrail block on both. Microsoft Entra ID sign-in with group lookup ran live on all three (including removal from a group and return), Google sign-in on GKE; Google Workspace groups are tested against a fake only. Autoscaling was load-tested on all three (64 concurrent calls, up to the max and back to 1, no 5xx). Each environment was torn down afterwards. Earlier releases did the same on GKE and AWS since 0.5.x. Everything below is written so those lines stay findable.
Build and deploy your own MCP tools across zones, on GKE, EKS or AKS. Ramen turns a git repo of tools, resources and prompts into a fleet of MCP workers behind a cloud load balancer, and keeps who-can-call-what, secrets, canary gates and the audit trail in one console. It is a platform for your tools, not a gateway in front of someone else's. Each worker pairs a Rust MCP node (Streamable HTTP and gRPC, bearer auth, IP allow-lists, health, logs) 1:1 with a Python 3.12 runtime that pip-installs and runs your code. One console manages groups (tenants), environments, zones, secrets, canary deploys, autoscaling, IP rules, logs, audit and backups — in the browser or through an API key.
Git → bucket → worker. Deploy syncs the repo to a bucket; workers load by content hash. No git creds on pods.
Canary by default, gated. Roll a canary, smoke-test
tools/list, diff its tool schemas against stable (breaking changes stop unless you saybreaking: true), run the repo's golden cases, then roll stable. Failure leaves stable untouched.Multi-zone from day one. Group → Environment → Zone → Worker; the LB routes on
ramen-group/ramen-zonemetadata, so one client config works for every zone.Per-tool access and a drift signal (0.7.2). Decide per environment who may see and who may call each tool (group keys, group admins, viewers, MCP users); clients learn about a rollout through
tools/list_changed; the group page shows how often the same caller repeated the same call, with a warning threshold.Guardrails inside the worker (0.7.5). Opt a tool into a pre-call and post-call check that runs in the Python runtime next to it: NVIDIA NeMo Guardrails rails (deterministic or LLM-backed, your model and key) or a
policy.pyof your own. A blocked call is a readable tool error, the tool never ran or its output never left the pod, and the access log says which rail. Per tool, fail-closed by default.Your identity provider decides, your admins override (0.7.5). Add Microsoft Entra ID, Google Workspace or any OIDC provider on the Config page; read a person's groups from Graph or Cloud Identity at sign-in, map them to roles per group, and keep what admins set by hand. Any IdP on any cloud.
Custom roles (0.7.5). Define
analyston the Viewer base, use it in memberships, provider rules and per-tool access; the person's token carries it to the worker. Mark a role OAuth only and its holders cannot use a password.Autoscaling by Ramen (0.7.5). Each zone runs between 1 worker and a max that super admins and group admins set. The console scales up fast under load and back down when quiet, the same on all three clouds, with no HPA and every step in the audit log. How it decides.
Three clouds (0.7.5 adds AKS). Terraform and Helm for GKE (Gateway, Cloud Armor), EKS (ALB, WAF) and AKS (Envoy Gateway, Let's Encrypt, Table Storage, Key Vault, workload identity). The same console and guards on each.
Enterprise controls. A role per group (Group Admin, Viewer, MCP User or one you define) plus global super admins,
rmk_MCP keys andrmn_API keys stored as salted hashes, IP rules (per zone at the node, and at the edge: Cloud Armor, WAF or an Envoy policy), secrets that are never displayed, an audit line for every action.Transport: Streamable HTTP at the edge, gRPC inside.
POST /mcpfor any client that can make an HTTP request;ramen.v1.Mcp/Callfor teams that want gRPC internally. One set of guards serves both — the same functions, spelled401 / 403 / 429on one andUNAUTHENTICATED / PERMISSION_DENIED / RESOURCE_EXHAUSTEDon the other — so the two paths cannot drift.Cheaper: one Deployment per zone, not one per tool. A worker is one Rust node plus one Python runtime that loads every tool of the group. A team with thirty small tools runs them on one Deployment per zone (two pods with a canary), behind one load balancer. Container-per-MCP-server designs run thirty. Ramen's autoscaling adds pods for load, not for tool count.
Secure, in one sentence. User code never runs in the process that holds the keys and does the auth: the Rust node checks every call and hands the message to a separate Python process it can kill and respawn.
Built on how organizations work
Groups own tools in git, environments pin a ref and a set of zones, people hold a role per group, agents and clients sign in as themselves, and every deploy is a canary, a smoke test and a rollout. Underneath it is gRPC, JSON-RPC 2.0 and a Rust node; you code in Python. The longer argument is How it works and why.
Start here · the demo group repo ramen-demo-mcp-group (point a group at it and press Deploy) · the stdio bridge ramen-mcp-bridge on PyPI (
uvx ramen-mcp-bridge@latest; signs you in with--oauthor carries a group key) · HTTP clients such as Claude Code, Cursor and VS Code need no bridge with a group key, and Claude Code can also sign you in with OAuth. See Connect a client. Every feature and where it is managed: How it works.
Related MCP server: MCP Manager
Quickstart (local, 5 commands)
Needs Docker with compose v2, uv, git and make. The first run builds two images and takes three to five minutes.
git clone https://github.com/bkraad47/ramen && cd ramen
make up # Firestore emulator + console https://localhost:8443 + one worker (localhost:8080: Streamable HTTP + gRPC)
make demo # zone, group `demo` from the demo repo, an rmk_ key, a canary deploy, then a tools/call
# PASS: demo_calculator_tool(2,3,add) -> 5 <- the success line
open https://localhost:8443 # self-signed cert; login admin@ramen.local / changeme-ramen
make down # stop and remove volumesmake demo is safe to re-run: an existing zone, group or environment answers "exists" and a fresh key is
generated each time.
Then connect your own client with an rmk_ MCP key (Groups → demo → Generate key → Deploy). It is a URL and
a header — put the key in RAMEN_MCP_KEY and drop this into any mcpServers config:
{"mcpServers": {"ramen-demo": {"url": "http://localhost:8080/mcp",
"headers": {"Authorization": "Bearer ${RAMEN_MCP_KEY}", "ramen-group": "demo", "ramen-zone": "local"}}}}Anything that can make an HTTP request is a client:
curl -s http://localhost:8080/mcp -H "Authorization: Bearer $RAMEN_MCP_KEY" -H 'Content-Type: application/json' \
-H 'ramen-group: demo' -H 'ramen-zone: local' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"demo_calculator_tool","arguments":{"var1":2,"var2":3,"func":"add"}}}'
# {"jsonrpc":"2.0","id":1,"result":{"content":[{"type":"text","text":"5"}],"isError":false}}Clients that only speak stdio use the bridge, which forwards to the same worker over gRPC:
# its own package (github.com/bkraad47/ramen-mcp-bridge); uvx runs the newest release without installing it
RAMEN_MCP_KEY=<the key shown once> uvx ramen-mcp-bridge@latest --target localhost:8080 --insecure --group demo --zone local
rmk_MCP keys go to workers (Authorization: Bearer, on HTTP or as gRPC metadata) and are generated on the group page.rmn_API keys go to the console (X-Ramen-Api-Key) for automation and are generated on the API keys page. They are not interchangeable. For a token scoped to one person rather than a shared key, register an OAuth client on the Config page: the worker's401tells an OAuth-capable client where to sign in. Full walkthrough with themcpSDK and a rawgrpcurlcall: Get started (also indeploy/local/README.md).
Connect a client
Two ways in, through the same guards. Streamable HTTP: the client talks to https://<edge>/mcp itself. The
bridge (ramen-mcp-bridge): a small stdio program the client starts; it speaks gRPC to the edge and, for a person,
does the browser sign-in. Ramen has no dynamic client registration, so a person's OAuth sign-in works over HTTP in
Claude Code (it takes a client id) and through the bridge everywhere else. Admins do not have to type any of this:
the group page's How MCP users sign in card prints every command filled in for the install, zone by zone.
Who signs in | Claude Code | Cursor, Claude Desktop | VS Code / GitHub Copilot |
A group key (agents, CI) | HTTP: | Cursor: HTTP, | HTTP: |
A Ramen password account | HTTP with | bridge | bridge |
Microsoft Entra ID or Google Workspace | same as a password account: the console's login page hands off to the provider | bridge | bridge |
Group key over Streamable HTTP
claude mcp add --transport http ramen-demo-a https://<edge>/mcp \
--header "Authorization: Bearer <rmk_ key>" --header "ramen-group: demo" --header "ramen-zone: a"{"mcpServers": {"ramen-demo-a": {"url": "https://<edge>/mcp",
"headers": {"Authorization": "Bearer ${RAMEN_MCP_KEY}", "ramen-group": "demo", "ramen-zone": "a"}}}}That is Cursor's ~/.cursor/mcp.json; VS Code's .vscode/mcp.json takes the same entry under "servers" with
"type": "http".
A person, over Streamable HTTP (Claude Code). A super admin registers a client on Config → OAuth clients
(redirect http://localhost/callback) and gives the person a role in the group; MCP User is enough.
claude mcp add-json ramen-demo-a '{"type":"http","url":"https://<edge>/mcp",
"headers":{"ramen-group":"demo","ramen-zone":"a"},
"oauth":{"clientId":"<client id>","scopes":"mcp:demo:a"}}'Then /mcp in Claude Code and sign in: a Ramen password, or the Entra ID / Google button when a super admin has added
the provider; a role marked OAuth-only allows only the provider.
Through the bridge, with a key or as a person
claude mcp add ramen-demo-a -- uvx ramen-mcp-bridge@latest --target <edge>:443 --tls --key <rmk_ key> --group demo --zone a
claude mcp add ramen-demo-a -- uvx ramen-mcp-bridge@latest --target <edge>:443 --tls \
--oauth https://<edge> --client-id <client id> --group demo --zone a{"mcpServers": {"ramen-demo-a": {"command": "uvx", "args": ["ramen-mcp-bridge@latest",
"--target", "<edge>:443", "--tls", "--oauth", "https://<edge>", "--client-id", "<client id>",
"--group", "demo", "--zone", "a"]}}}The JSON is Cursor's and Claude Desktop's; in VS Code put it under "servers" with "type": "stdio". Bridge 0.3.0
keeps a person's token in memory (--keychain for the OS keychain, --token-file for automation), forgets it after
30 idle minutes, and when a sign-in is needed mid-session answers the call with error -32001 and the sign-in link,
so the AI client can hand it to the person. @latest keeps a cached uvx from running an old bridge. Details:
Connect with OAuth ·
Entra ID and Google Workspace ·
Transport.
Screenshots
Login | Dashboard, load per zone and group |
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Group: environments, deploy jobs, zones/workers, MCP keys | Deploy job with streamed log |
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Secrets (names only, values never shown) | Logs (one JSON line per MCP call, downloadable) |
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Identity providers: Entra ID with group lookup and role rules | Custom roles, one OAuth-only |
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Zone actions: count, max, last autoscale decision | Zones and workers with the max per group |
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Transport, and what secures each hop
Workers speak Streamable HTTP (POST /mcp, one JSON-RPC 2.0 message per request, MCP spec 2025-06-18) and
gRPC (ramen.v1.Mcp/Call, one message as bytes body) on the same port (contract §16,
§11). MCP itself is unchanged — your client and your tools see the standard messages. The two
transports share one implementation of every check: the HTTP handler turns the request headers into the same
metadata map and calls the same guard and dispatch functions the gRPC service calls, so a check added to one is on
both or on neither.
Why the node is Rust. A worker runs two processes with one job each. ramen-node (Rust + tonic) owns what must
not be slowed down or broken by user code: the gRPC surface, key checking, source-range checking, the blocked-name
filter, concurrency bounds, deadlines, health and the access log. It is a small static binary with no interpreter
and no user code in its address space. ramen_runtime (Python 3.12) owns what users write: pip install, validation,
secret substitution, the call. They talk over newline-delimited JSON-RPC on stdin/stdout (§2),
so there is no extra socket to secure, and the runtime is killed after an idle timeout — a crash or leak in tool
code costs one respawn, not the process holding the keys.
Hop | What protects it |
client → edge (HTTP) | TLS at the load balancer; the credential in |
client → bridge → edge (stdio) | a child process on the client's own machine, speaking gRPC to the edge; plaintext unless |
edge → node | TLS ends at the load balancer; h2c to the node unless the node has its own certificate; Cloud Armor (GCP) or WAF (AWS) IP rules, one policy per group; an Envoy |
every | constant-time key compare; source range against the right |
edge → node, without a key | only |
anything else → the pod | the worker |
node → runtime | stdio inside the pod; no network surface |
runtime → bucket | the zone's own cloud identity (GCP service account with Workload Identity, AWS IAM role with IRSA, Azure managed identity with workload identity), scoped to the group's prefix (on Azure the container) and secrets |
runtime → your tool | opt-in guardrails per tool: NeMo rails or a |
console → node | cluster-internal, never through the load balancer; |
Five details behind that table matter in practice. The origin allowlist is empty by default, so every browser
Origin is refused until you add one. The source-range check reads the x-forwarded-for entry a proxy appended
(hop count 2 on GCP, 1 on AWS and AKS), and a wrong count denies rather than admits. The allowlist itself defaults to
everything until you set IP rules. An IP lock must include the console's own range, because a deploy smoke-tests
tools/list as an ordinary call. grpc.health.v1.Health is deliberately unauthenticated so load balancers can
probe it, and reports SERVING only once the runtime has loaded the group's code.
What is verified, in five lines.
Both transports through every guard, on real node processes, in CI on Linux and on a Windows runner that builds the node natively (0.5.0).
0.7.5 live on GKE, EKS and AKS: the 33-check run on each, Entra ID sign-in with group lookup on each, autoscaling load tests on each, every environment torn down afterwards.
The GCP and AWS paths live every release since 0.5.x, with two zones, OAuth, the Redis throttle and real teardown in 0.6.0 and 0.6.1.
Covered by tests only, never on a real load balancer: node TLS, and the size and in-flight caps.
By design, a group's code runs in a process that holds that group's secrets. Isolation between groups is the pod, the namespace and the per-zone identity.
Full write-up: Transport and what secures each hop.
Deploy to the cloud
Target | Status | Guide |
GCP — GKE Autopilot, Firestore, GCS, Secret Manager, global HTTPS LB (GKE Gateway, header-routed gRPC and Streamable HTTP, gRPC health checks), Cloud Armor | verified on a throwaway project every release, most recently 0.7.5: 33 checks, Entra ID and Google sign-in, autoscaling load tests | |
AWS — EKS, DynamoDB, S3, Secrets Manager, ALB (gRPC and HTTP/1.1 target groups), WAF (Terraform or CloudFormation) | applied to a real account since 0.5.6, most recently 0.7.5: 33 checks, Entra ID sign-in, autoscaling load tests | |
Azure — AKS (workload identity), Table Storage, Blob Storage, Key Vault, Envoy Gateway (Gateway API, header-routed gRPC and Streamable HTTP) with a Let's Encrypt certificate | new in 0.7.5, verified live in | |
Local — docker compose | CI e2e on every push |
Bring-up is the same shape on each cloud. On GCP it is terraform apply → make push → helm upgrade --install → add a zone and a group in the
console → Deploy. About 25 minutes, mostly waiting for GKE and the load balancer. The load balancer gets a
publicly-trusted certificate automatically (a free sslip.io hostname derived from the static IP — no domain to
buy, since 0.5.5; on AKS from Let's Encrypt, since 0.7.5). Clients then use https://<public_hostname>/mcp with Authorization: Bearer rmk_ and the
ramen-group / ramen-zone headers (the same address serves the console and, by those headers, every zone);
stdio-only clients point the bridge at <public_hostname>:443 --tls — no --ca, nothing to import.
Write your own tools
Full guide with the demo repo, env.yaml and local development: The MCP repo, structure and local development.
A group repo is any git repo with mcp/tools/<name>/<name>.py + <name>.json (and resources/, prompts/,
requirements.txt). Start from ramen-demo-mcp-group; the
contract is in the MCP repo page. Secrets are referenced as
{{$group.NAME}} and substituted by the runtime at call time. Nothing about the transport leaks into tool code.
Declare output and the worker publishes it as outputSchema and validates every result; add mcp/tests.yaml and
your cases gate every deploy (0.7.0). Add mcp/guardrails.yaml and NeMo rails or a policy.py check the arguments
before a tool runs and its result before it leaves the worker (0.7.5, Guardrails).
Repository
Dir | What |
FastAPI + Jinja2 + HTMX manager UI and | |
Rust MCP server node (tonic: | |
Python 3.12 runtime (loads protos, pip installs, runs calls, resolves secrets, runs guardrails) | |
| |
compose, Helm charts, Terraform (GCP, AWS, Azure), CloudFormation | |
Cloud-ops agent skills: deploy-gcp, deploy-aws, rotate-keys, backup-restore, scale-zone | |
Black-box conformance (gRPC + bridge), e2e and cloud suites | |
This site's sources; |
Architecture: ARCHITECTURE.md · Changes: CHANGELOG.md · Versions: tracker
Develop
make test # runtime-py and console (pytest, 90% coverage gate) plus node-rs (fmt, clippy, test)
make test-harness # tests/: conformance + e2e (skips without a running stack)
make proto # regenerate Python stubs from proto/ (Rust stubs build via tonic-build)
make demo-worker # node + runtime locally without Docker
uv run --project docs --group docs mkdocs serve # docs at http://127.0.0.1:8000Contributing
See CONTRIBUTING.md — use it, fork it, change it, with attribution; renaming it as a new commercial product of your own is not acceptable. Related repositories and which versions go together: Releases.
License
BSD-3-Clause © 2026 Raad. See LICENSE.
Available Tools
3 toolsdemo_calculator_toolA
Add, subtract, multiply or divide two numbers and return the result as a number. Pure and stateless: no side effects, no network, no auth beyond the group's key, no rate limit of its own. Division by zero returns a tool error (isError) with a one-sentence message instead of a value. Use it when an exact arithmetic result matters; for anything beyond one binary operation, chain calls or compute in the model.
| Name | Required | Description | Default |
|---|---|---|---|
| func | Yes | operation to apply as var1 <func> var2 | |
| var1 | Yes | first operand (left-hand side); any finite number | |
| var2 | Yes | second operand (right-hand side); must be non-zero for divide |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes | var1 <func> var2 |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so: pure/stateless, no side effects, no network, auth only via the group key, no own rate limit, and the division-by-zero path returns isError with a one-sentence message rather than a value. That is exactly the behavioral context an agent needs.
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?
Four short sentences, front-loaded with purpose, then behavior, then the error case, then routing guidance. Every sentence earns its place 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?
An output schema exists so return values need no explanation, and the description still covers the one non-obvious edge case (division by zero producing a tool error). Nothing needed to call this tool correctly is missing.
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 100%, so the schema already documents func's enum and both operands (including the non-zero constraint for divide). The description adds only the 'two numbers' framing and the left/right ordering implied by var1 <func> var2, so baseline 3 applies.
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?
States a specific verb set (add, subtract, multiply, divide) plus the resource (two numbers) and the return type. It is trivially distinguishable from the arithmetic-free siblings unit_convert and word_count.
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 names when to use it ('when an exact arithmetic result matters') and when not to ('for anything beyond one binary operation, chain calls or compute in the model'). The alternative path is stated, not implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
unit_convertA
Convert a value between units of length (mm, cm, m, km, inch, foot, yard, mile), mass (mg, g, kg, tonne, ounce, pound) or temperature (celsius, fahrenheit, kelvin) and return the converted number. Pure and stateless, no side effects, no auth beyond the group's key. Units are matched case-insensitively by the names listed; an unknown unit or a conversion across families (length to mass) returns a tool error (isError) naming the accepted units. Use it for exact factor-based conversions; it does not handle currencies, dates or compound units such as km/h.
| Name | Required | Description | Default |
|---|---|---|---|
| value | Yes | the quantity to convert, in from_unit | |
| to_unit | Yes | unit of the result, same family as from_unit | |
| from_unit | Yes | unit of value, e.g. km, pound, celsius |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes | value expressed in to_unit |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and does so: pure/stateless, no side effects, no auth beyond the group key, case-insensitive unit matching, and explicit error semantics (tool error with isError naming accepted units on unknown unit or cross-family conversion). That is unusually complete behavioral disclosure for a stateless utility.
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?
Front-loads purpose and return value in the first sentence, then behavior, then routing guidance. Every clause carries distinct information (units, families, case rules, error behavior, exclusions) with 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?
An output schema exists, so return formatting need not be elaborated, yet the description still states the result is a converted number. Combined with the auth, statelessness and error details, an agent has everything needed to call this correctly with three required parameters.
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 100%, so the baseline is 3; the description still adds real value by listing the accepted unit names, stating that matching is case-insensitive, and defining the cross-family failure mode for from_unit/to_unit. It stops short of documenting numeric ranges or precision/rounding behavior for value.
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?
Names a specific verb (convert) and resource (units of length, mass, temperature) and enumerates the exact accepted unit symbols, so an agent knows precisely what domain this covers. It also carves itself apart from a generic calculator sibling by excluding currencies, dates and compound units like km/h.
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?
"Use it for exact factor-based conversions; it does not handle currencies, dates or compound units such as km/h" gives an explicit when-to-use plus a concrete when-not-to-use set. Although it does not name the sibling tools, the exclusions are the operationally relevant routing signal and leave nothing to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
word_countA
Count the characters, words and lines of a text and return the three counts as an object. Pure and stateless: the text is not stored or sent anywhere; no auth beyond the group's key. Words are whitespace-separated runs; lines are newline-separated (a trailing newline does not add a line); characters are Unicode code points including whitespace. Use it when a model needs an exact count rather than an estimate; it does not tokenise or detect language.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | the text to count; may be empty, which yields zeros |
Output Schema
| Name | Required | Description |
|---|---|---|
| lines | Yes | newline-separated lines; 0 for an empty text |
| words | Yes | whitespace-separated runs |
| characters | Yes | Unicode code points, whitespace included |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden, and it delivers: 'Pure and stateless: the text is not stored or sent anywhere; no auth beyond the group's key.' It also pins down exact counting semantics (whitespace-separated words, newline-separated lines without a trailing-newline artifact, Unicode code points including whitespace), which is unusually complete for a no-annotation 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?
Four sentences, front-loaded with what it counts and what it returns, then safety, then precise definitions. Every sentence contributes something an agent cannot get from the schema, with 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?
An output schema exists, so return-value documentation is unnecessary, yet the description still names the object shape. For a stateless one-parameter utility it covers purpose, selection, safety/auth, and edge-case semantics, leaving nothing an agent needs before calling it.
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 100% and the single parameter is already documented, including the empty-string case. The description's word/line/character rules govern how the result is computed rather than adding new meaning to the `text` parameter, so the baseline of 3 for high schema coverage is the right level.
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?
States a precise verb and resource ('Count the characters, words and lines of a text') plus the exact return shape ('return the three counts as an object'). It is trivially distinguishable from the unrelated siblings demo_calculator_tool and unit_convert, and no other tool in the set could be confused with it.
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 gives the selection condition ('Use it when a model needs an exact count rather than an estimate') and the when-not cases ('it does not tokenise or detect language'). An agent knows both when to reach for this tool and which related tasks it must not use it for, without any inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
v0.7.1- Changed
demo_calculator_tool4 fields changed- changed
Input schema / properties / func / descriptionPrevious value: -"operation"New value: +"operation to apply as var1 <func> var2" - changed
Input schema / properties / var1 / descriptionPrevious value: -"first operand"New value: +"first operand (left-hand side); any finite number" - changed
Input schema / properties / var2 / descriptionPrevious value: -"second operand"New value: +"second operand (right-hand side); must be non-zero for divide" - changed
Output schema / (root)Previous value: -nullNew value: +{ + "properties": { + "result": { + "description": "var1 <func> var2", + "type": "number" + } + }, + "required": [ + "result" + ], + "type": "object" +}
- Added
unit_convert - Added
word_count
1 tool update
v0.1.0- First observed
demo_calculator_tool
TDQS
Scored across 3 tools
Each tool targets a completely different operation: arithmetic (demo_calculator_tool), unit conversion (unit_convert), and text counting (word_count). There is no overlap or plausible misselection between them, and each description clearly states its scope and limits.
All names are snake_case and readable, and two follow a clean noun_verb pattern (unit_convert, word_count). The outlier demo_calculator_tool adds an unnecessary 'demo_' prefix and '_tool' suffix, a minor deviation from the otherwise consistent convention.
Three tools is on the thin side for a general-purpose utility server, but each is distinct and clearly earns its place with no filler. It is reasonable if the server is intentionally a small demo/utility set.
Each individual tool is self-contained and complete for its narrow function, but the server has no coherent domain, so there is no lifecycle or CRUD surface to evaluate. As a set it reads as a few unrelated utilities rather than a complete toolkit.
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