How do I use tero-mcp-lite?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@tero-mcp-lite find all citations for DN-87 in the corpus" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

tero-mcp-lite

by Aphelion-Development

Overview Schema Related Servers Score Discussions

Rust

Local

tero-mcp-lite

Security

A lightweight, portable MCP (Model Context Protocol) server over a Tero corpus index.json — the Python-only counterpart to the Rust tero-mcp binary in tero-rs (src/bin/tero-mcp.rs in the tero-rs crate, DN-87 / E39-1). It answers cited, provenance-carrying queries about a project's corpus (docs, decisions, issues, changelog, skills) over stdio JSON-RPC 2.0, with the same never-silent-refusal contract: an answer without a resolvable citation is a typed refusal, never a silent empty result (DN-87 §6.2).

This package is deliberately "lite": it has no runtime dependencies (stdlib json/argparse only), reads a committed index.json rather than building one, and implements only the Layer-1 (deterministic-index) query surface — no VSA/Layer-2 semantic memory. It is meant to drop into any repo that has (or generates) a Tero-shaped index, not just Mycelium's own.

What it is / isn't

Is: a thin, honest query engine + MCP stdio front over a pre-built index.json. Five query kinds (query_by_id, query_by_status, query_by_kind, cross_ref, text_search) plus cite/explain/identify/refresh — nine tools total, matching the Rust server's tool surface and JSON envelope shapes exactly (see "Matching the Rust server" below).
Isn't: an index builder. Regenerating index.json for your repo is a separate concern — see GENERATING-AN-INDEX.md.
Isn't: Layer-2 (VSA semantic search). identify always reports layer2_enabled: false. If you need that, use the full Rust tero-rs tero-mcp binary this package delegates to when present. Memory tools (memory_store / memory_retrieve / memory_consolidate) are tero-rs-only — build with Cargo feature memory, scopes memory-read / memory-write, runtime TERO_MEMORY_ENABLED, TERO_MEMORY_DB, optional TERO_MEMORY_MODEL — see docs/MEMORY_TOOLS.md; lite parses the same scopes but refuses memory tools/call honestly.

Related MCP server: mcp-stdio

Install

Requires Python >= 3.11 and uv.

cd packages/tero-mcp-lite
uv sync

This creates .venv and resolves the (dev-only) dependency group — pytest for the test suite. The runtime server itself has zero third-party dependencies; uv sync --no-dev installs nothing but the package itself.

Run directly:

TERO_TOKENS='devtoken:read' uv run tero-mcp-lite --index /path/to/index.json

Or via the console-script entry point once installed (uv tool install . / pip install .):

TERO_TOKENS='devtoken:read' tero-mcp-lite --index /path/to/index.json

--index defaults to docs/tero-index/index.json (relative to the process's working directory), and can also be set via TERO_INDEX_PATH.

Register in `.mcp.json` (persistent use in this repo)

The repo-root .mcp.json already registers a tero server (see the top-level file). The entry:

{
  "mcpServers": {
    "tero": {
      "command": "uv",
      "args": [
        "run", "--project", "packages/tero-mcp-lite",
        "tero-mcp-lite", "--index", "docs/tero-index/index.json"
      ],
      "env": {
        "TERO_TOKENS": "local-dev:refresh"
      }
    }
  }
}

Claude Code (and any other MCP-aware client) picks this up automatically for sessions rooted at the repo. Rotate TERO_TOKENS for anything beyond local/dev use — never commit a real secret token; the placeholder above is intentionally a non-secret local-dev value, matching the Rust server's own "refuses to start without tokens, but the token value itself is just an opaque bearer string" model.

Auth

Exactly like the Rust server: set TERO_TOKENS (or TERO_TOKENS_FILE, a path to the same grammar) — a whitespace/comma-separated token:scope list, e.g. s3cr3t:read other:refresh. refresh implies read. The server refuses to start with no tokens configured — there is no anonymous default. Every tools/call carries its own token argument (checked against the operation's required scope before dispatch) — auth is per-call, not per-connection, matching the Rust server's model exactly.

Generating an index for any repo

See GENERATING-AN-INDEX.md for the index.json schema and how to produce one — either with Mycelium's own Rust tero-index binary, or a from-scratch tool in your own repo that emits the same shape.

Matching the Rust server

This package was built by reading the tero-rs tero-mcp front (src/bin/tero-mcp.rs and src/front/{core,mcp,auth}.rs, plus src/model.rs / src/query.rs) and mirroring:

the same nine tools, same inputSchema shapes, same required arguments;
the same JSON-RPC transport: newline-delimited JSON-RPC 2.0 over stdio, initialize → tools/list → tools/call, MethodNotFound (-32601) for anything else;
the same envelope shapes (answer/citations/explain/refusal/error);
the same refusal semantics: no_match / unknown_anchor / no_text_match, each carrying candidates_scanned and a human-readable message — DN-87 §6.2's contract enforced the same way (an Answer-shaped dataclass simply cannot be constructed with zero items; every query function raises a typed Refusal instead); the same cross_ref BFS over depends_on/doc_refs edges (issue-only depends_on targets, corpus:DOC[#anchor]-only resolvable doc_refs, same dedup-suffix anchor-matching rule, same MAX_CROSSREF_DEPTH=6 clamp reported in Explain.query, never silently);
the same text-search scoring (id match x4 + title match x3 + summary match x1 per matched term, ties broken by canonical (family, file, line, anchor) order, capped to 20 results);
the same token-scoped auth model (per-call token argument, read/refresh scopes, refuse to start with no tokens).

The wire-visible shapes checked by tests/test_rust_parity.py — tool descriptor JSON (values and key order), the JSON-RPC error code mapping, auth-error message wording, refusal variant tags, and the process exit codes — are transcribed verbatim from the Rust source and asserted byte-for-byte equal; a diff there means real drift, not a documentation gap. Where behavior could plausibly diverge structurally (the cross_ref clamp-reporting rule, the is_dedup_suffix_of anchor-matching grammar, the Family sort-rank order used for the canonical key, the text-search scoring weights) this package copies the Rust logic structurally, not just by description, specifically to avoid silent semantic drift.

The one deliberate, documented divergence is the identify tool's payload (not its tool descriptor, which does match): name/engine/summary describe this Python server's own identity ("tero-mcp-lite", Layer-1-only) rather than claiming to be the Rust binary — an honest identity beats a byte-identical lie. tests/test_rust_parity.py does not assert this field, and the serverInfo.name in initialize is likewise "tero-mcp-lite", by design.

The parity test suite is a transcription-derived check, not a live differential (no checked-out Rust source is available at Python test/CI time here) — see "Framework — remaining tasks" below for the live-differential follow-up.

Why a minimal implementation instead of the official `mcp` Python SDK

The official mcp SDK (PyPI mcp) does install cleanly via uv with no version conflicts — it was evaluated. It was not used here for three concrete reasons:

Weight vs. the "lite"/portable goal. mcp pulls in ~30 transitive packages (pydantic, pydantic-core, cryptography, starlette, uvicorn, sse-starlette, ...) — mostly HTTP/SSE transport machinery this package doesn't use (stdio only). A package meant to be zipped and dropped into an arbitrary repo is better served staying small.
Exact semantic control. The Rust server's auth model is unusual for MCP: the bearer token is a per-tools/call argument, not a transport-level header, and an auth/bad-request failure is a top-level JSON-RPC error, not an isError:true tool result. The SDK's high-level @server.call_tool() decorator catches all exceptions (including a raised McpError) and turns them into isError:true tool results by default — matching the Rust behavior exactly would mean bypassing that decorator and registering a raw low-level request handler anyway, which erodes most of the SDK's convenience value for this specific shape of server.
Zero dependency-conflict risk, trivially auditable. ~700 lines of pure-stdlib Python across 5 files is easy to read start to finish and carries no supply-chain surface beyond the interpreter.

uv is still used as a real project/dependency manager (uv.lock, [dependency-groups] dev carrying pytest) — this isn't a bare script; it's satisfied at the project-management layer rather than by adding runtime weight the package doesn't need. If a future maintainer wants full MCP-spec coverage (resources, prompts, sampling, elicitation, streamable-HTTP transport, ...), switching to the mcp SDK is a reasonable evolution — see "Framework — remaining tasks" below.

Adding a new tool (the registry pattern)

src/tero_mcp_lite/mcp_server.py derives both tools/list's descriptors and tools/call's dispatch + auth-scope check from one declarative TOOL_REGISTRY: dict[str, ToolSpec]. Adding a tool means adding one ToolSpec — nothing else in the file changes.

from tero_mcp_lite.mcp_server import ToolSpec, TOKEN_ARG

def _handle_my_tool(state: McpState, args: dict) -> dict:
    ...  # read args, touch state.report, return a JSON-able dict

my_tool = ToolSpec(
    name="my_tool",
    description="One line: what it does.",
    properties={"some_arg": {"type": "string"}, "token": TOKEN_ARG},
    required=("some_arg", "token"),
    handler=_handle_my_tool,
    # scope=Scope.REFRESH,  # omit for the default (read-only)
)

then add my_tool to the specs list in _build_registry(). That's it:

tools/list advertises it automatically (ToolSpec.descriptor(), derived — see _tool_descriptors()).
tools/call authorizes against scope (default Scope.READ) and dispatches to handler automatically (_handle_tools_call()).
A new query kind (as opposed to a new top-level tool) is a smaller change: add the kind to tero_mcp_lite.query.Query.parse + a _<kind>() function in query.py (mirroring the existing _by_id/_by_status/_cross_ref/_text shape — refuse on an empty match set, always attach an Explain trace), then wire a ToolSpec (or extend cite/explain's kind argument, since those already forward whatever kind string they're given) exactly as above.
Extend tests/test_rust_parity.py's RUST_TOOL_DESCRIPTORS (and, if the tool has a Rust-side twin, transcribe its exact wording from source) so the new tool's shape stays pinned too.

Tests

uv run pytest

Covers: a JSON-RPC round-trip (initialize → tools/list → query_by_id returning a cited answer), a refusal test (an uncited query returns a typed refusal, never an empty result), unit coverage for auth/query/model, and tests/test_rust_parity.py — the Rust-source-transcribed byte-level checks described above. All fast and fully offline (an in-memory synthetic index — no network, no real repo required).

Framework — remaining tasks

A checklist for whoever picks this up next (in this repo or an extracted one):

Byte-level parity harness — transcription version. tests/test_rust_parity.py pins the tool descriptor JSON (values + key order), the JSON-RPC error code mapping, auth-error wording, refusal variant tags, and exit codes as verbatim transcriptions of the Rust source. This caught two real wording bugs on introduction (an identify tool description with an extra clause, ... where the Rust source uses …) — evidence the check has teeth.
Byte-level parity harness — live differential. The stronger version: a test that actually runs both the Rust tero-mcp binary and this package over the same index.json and diffs their JSON-RPC responses field-by-field, so a future Rust-side wording change is caught automatically instead of requiring a human to notice and re-transcribe. Needs a Rust toolchain + a built tero-mcp binary available at test time, which this repo's own CI does not provide (this package is meant to be extracted/dropped into other repos, most of which won't have the mycelium Rust crate either) — plausibly a mycelium-repo-side CI job instead of a tero-mcp-lite-side one.
HTTP front. The Rust crate also ships tero-http (a plain HTTP/JSON front sharing the same core). This package only implements the MCP/stdio front; an HTTP front (e.g. http.server or a minimal ASGI app) is a natural, still-lightweight follow-up if a non-MCP client needs it.
Layer-2 / VSA. Deliberately out of scope (layer2_enabled is hardcoded false). If the VSA semantic layer (DN-87 §2 fork 1) ever needs a Python-native front, that's new work, not a gap in this package.
refresh hot-reload race. _refresh swaps state.report between requests; this server is single-threaded/single-client over stdio (matching the Rust server's own single-threaded stdio model), so there's no concurrency hazard today — flag if this is ever adapted to a multi-client transport.
Consider the mcp SDK if/when this needs full MCP-spec surface (resources, prompts, sampling) beyond tools — see the tradeoff write-up above; the SDK does install cleanly with uv.
Packaging polish. Currently zipped as source (uv sync on first use in the target repo). A uv build-produced wheel could be attached to a release instead, if the target repo prefers not to keep a pyproject.toml + src/ tree around.
Security scans + hardening. This package has not been run through a dedicated supply-chain/security scan in this environment (api.x.ai and most external scan tooling are unreachable from this repo-scoped session) — see packages/GROK-HANDOFF.md at the repo root for the runbook to do that on infrastructure that can reach it.

Contact

Maintainer contact for this package: tz-dev@vectorweight.com · github.com/tzervas. (This is a swap-able project email/handle — update pyproject.toml's [project.authors]/[project.urls] and this section if ownership moves.)

License

MIT — see the repository root LICENSE (or add one in an extracted repo; ADR-022 §7 / CONTRIBUTING §Licensing require MIT-only for first-party Mycelium artifacts, and this package inherits that posture as a Mycelium-repo artifact).

Status & roadmap

Semver + Releases

Current release: v0.2.0 (2026-07-21).

Source	Version
`pyproject.toml` / `tero_mcp_lite.__version__`	0.2.0
`rust/Cargo.toml` (`CARGO_PKG_VERSION` / MCP `serverInfo`)	0.2.0
GitHub Release / tag	v0.2.0

Prior: v0.1.0 baseline, v0.1.1 first package release. PyPI not published (install from git/uv path). Process + cites: docs/ROADMAP.md semver section.

Available Tools

9 tools

citeD

Citations only for a query (kind + its args, as query_*).

ParametersJSON Schema

Name	Required	Description
`kind`	Yes	id\|status\|kind\|cross_ref\|text
`depth`	No
`start`	No
`token`	Yes	bearer token (from TERO_TOKENS)
`value`	No

TDQS

D1.8/5.0

Behavior1/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention side effects, return format, authentication requirements, or whether the operation is read-only. The cryptic one-liner gives the agent no insight into the tool's runtime behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise as a single sentence, but it is under-specified and poorly structured. While brevity is positive, the sentence is cryptic and does not clearly convey the tool's purpose, making the conciseness counterproductive.

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

Completeness1/5

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

For a tool with 5 parameters, 2 required, and no output schema or annotations, this description is grossly inadequate. It does not explain return values, expected input combinations, or the meaning of 'citations' in this context, leaving the agent with insufficient information to use the tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 40% (kind and token have descriptions), and the description adds little. It says 'kind + its args' but does not explain depth, start, or value parameters, nor how they relate to specific kind values. The description fails to compensate for the undocumented parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Citations only for a query (kind + its args, as query_*)' is vague and lacks a clear verb-resource structure. It doesn't specify what 'citations' means or what the tool actually does with the query, making it hard to distinguish 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.

Usage Guidelines2/5

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

There is no explicit guidance on when to use 'cite' versus the sibling query tools (e.g., query_by_status, query_by_kind). The reference to 'query_*' implies a connection but does not explain the appropriate context or alternatives, leaving the agent without clear selection criteria.

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

cross_refA

Breadth-first walk of depends_on/doc_refs edges from a start id/anchor.

ParametersJSON Schema

Name	Required	Description
`depth`	No	hop count (default 1)
`start`	Yes
`token`	Yes	bearer token (from TERO_TOKENS)

TDQS

A3.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions BFS traversal but does not state whether the operation is read-only, what the output format looks like, if there are any limits on depth or result size, or whether authentication via token is required. This is a significant gap for a traversal tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that front-loads the core action and key parameters. It is concise and free of redundant information, earning every word.

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

Completeness2/5

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

For a graph traversal tool with no output schema and no annotations, the description is incomplete. It does not explain what the result contains, how depth affects the traversal, or how the token is used. It also lacks any guidance on when to choose this tool over siblings, making it insufficient for an agent to confidently invoke it in a complex workflow.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds meaning to the 'start' parameter by describing it as an 'id/anchor', which is missing from the schema. It also implicitly clarifies 'depth' as a hop count, though the schema already provides that. The token parameter is well-described in the schema. Overall, the description compensates for the missing start description without fully explaining all parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's function: a breadth-first walk over depends_on/doc_refs edges from a start id/anchor. This specific verb+resource+method distinguishes it from sibling tools like query_by_id (direct lookup) and text_search (text-based search). It is unambiguous and actionable.

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

Usage Guidelines3/5

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

The description implies when to use the tool—when you need to explore transitive dependencies or references from a starting node. However, it does not explicitly state when not to use it or compare to alternatives such as query_by_status or query_by_kind. The usage context is clear but lacks exclusions.

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

explainC

EXPLAIN trace only for a query (kind + its args, as query_*).

ParametersJSON Schema

Name	Required	Description
`kind`	Yes
`depth`	No
`start`	No
`token`	Yes	bearer token (from TERO_TOKENS)
`value`	No

TDQS

C2.4/5.0

Behavior2/5

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

No annotations are supplied, so the description carries the full burden. It only hints at non-execution with 'trace only', but does not explicitly state side effects, permissions, output format, or error behavior. This is insufficient for a tool with 5 parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence with no wasted words, which is structurally concise. However, it is too terse and uses jargon ('EXPLAIN trace', 'query_*') that obscures clarity, so it could be more informative while remaining concise.

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

Completeness2/5

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

With 5 parameters, no output schema, and no annotations, the description is incomplete. It does not mention return values, parameter details beyond a vague 'kind + args', prerequisites, or any contextual information. The link to query_* tools is the only context provided, which is insufficient.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 20% (only 'token' has a description). The description adds a hint that 'kind + its args' correspond to query_* tools, which gives some meaning to 'kind' and the other parameters, but it leaves depth, start, and value unexplained, providing minimal compensation for the low coverage.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description identifies an operation ('EXPLAIN trace') and a scope ('for a query (kind + its args, as query_*)'), which differentiates it from sibling query tools. However, the meaning of 'EXPLAIN trace' is ambiguous and relies on domain knowledge, making it only vaguely clear.

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

Usage Guidelines2/5

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

No explicit when-to-use guidance is provided. The reference to 'query_*' implies a connection to sibling tools, but it does not state when to choose this tool over query_by_kind, query_by_status, or others, nor does it mention any exclusions or alternatives.

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

identifyB

Server identity, version, and whether the Layer-2 gate is open.

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	bearer token (from TERO_TOKENS)

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description carries full responsibility for disclosing behavior. It only lists output items and does not mention side effects, safety, permissions, or any operational context. For a tool with a bearer token parameter, it does not state whether the operation is read-only or if any state changes occur.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, compact sentence that lists the three key outputs in a clear order. It contains no filler or redundant phrasing, making it appropriately sized and front-loaded.

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

Completeness4/5

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

For a simple one-parameter tool with no output schema, the description sufficiently covers return content (identity, version, gate status). It lacks broader context such as error conditions or prerequisites beyond the token, but the scope is simple enough that this is a minor gap.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% for the single parameter 'token', which is described as 'bearer token (from TERO_TOKENS)'. The description adds no parameter-specific meaning, so the baseline score of 3 applies since the schema already fully documents the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool provides server identity, version, and Layer-2 gate status. It names specific resources, though it lacks an explicit verb like 'get' or 'retrieve'. It does not explicitly distinguish from sibling tools, but its focus on server metadata sets it apart from query/search tools.

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

Usage Guidelines3/5

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

The description implies usage: when you need server identity, version, or gate status, this is the tool. However, it provides no explicit guidance on when not to use it or comparisons to alternatives like query_by_status or explain. There is no direct mention of alternatives or exclusions.

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

query_by_idA

Exact lookup by corpus id (RFC-0034, M-1015, DN-87, an issue id).

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	bearer token (from TERO_TOKENS)
`value`	Yes	the id to match

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden. It only discloses that the lookup is exact, but does not describe the return format, error behavior when no match is found, or any side effects. While it is reasonable to infer a read-only operation, that is not explicitly stated.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single sentence, front-loaded with the key fact ('Exact lookup by corpus id'), and parenthetical examples add clarity without bloat. Every word contributes value; there is no waste.

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

Completeness3/5

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

For a simple lookup tool with high schema coverage and no output schema, the description conveys the core purpose but omits what the tool returns (e.g., the full record) and the not-found behavior. It is minimally viable but leaves these gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema covers both parameters (token and value) with basic descriptions. The tool description adds meaning by clarifying that 'value' is a corpus id and provides concrete examples of valid formats, which goes beyond the schema's generic 'the id to match'.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs an exact lookup by corpus id, with specific examples of id formats (RFC-0034, M-1015, DN-87). This distinguishes it from sibling tools like query_by_status or query_by_kind, which lookup by other attributes, and from text_search, which implies fuzzy search.

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

Usage Guidelines3/5

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

The wording 'exact lookup by corpus id' strongly implies this tool is for when you have a known exact id to match. However, it does not explicitly state when not to use it or mention alternatives (e.g., use text_search for partial matches). The usage is implied rather than explicit.

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

query_by_kindB

All rows of a given kind (rfc, adr, note, issue, section, …).

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	bearer token (from TERO_TOKENS)
`value`	Yes

TDQS

B3/5.0

Behavior2/5

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

With no annotations, the description must carry the behavioral burden, but it only says 'All rows' which implies a read operation without filtering. It does not disclose authentication requirements, pagination, error handling, or whether the result is limited to a specific set of fields.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that gets straight to the point. No filler or redundant information.

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

Completeness2/5

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

The tool has no output schema and minimal annotations. The description does not explain what the response contains, how to handle invalid kinds, or when to prefer this over sibling tools. For a 2-parameter tool this is sparse, but not wholly inadequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 50%: 'token' is described, but 'value' is not. The description adds meaning by indicating 'value' is a kind and gives examples, but it uses an ellipsis and does not enumerate valid values or specify format, leaving ambiguity.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists all rows of a given kind, with examples of valid kinds (rfc, adr, note, issue, section). This distinguishes it from sibling tools like query_by_status or query_by_id, which operate on different criteria.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus alternatives. No mention of prerequisites, exclusions, or how it compares to query_by_status, query_by_id, or text_search.

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

query_by_statusB

All rows with a given status (Accepted, todo, done, …).

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	bearer token (from TERO_TOKENS)
`value`	Yes

TDQS

B3.3/5.0

Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does not mention whether matches are exact, case-sensitive, paginated, or how results are ordered. The description is too sparse to provide meaningful transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence with relevant examples, no filler. Although it is a sentence fragment, it communicates the essential functionality efficiently.

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

Completeness3/5

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

For a simple query tool, the description gives a minimal overview but omits details such as output format or any limitations. With no output schema and no annotations, more context (e.g., whether all fields are returned or if there is pagination) would be valuable. Still, the tool's simplicity keeps this from being severely inadequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema describes 'token' but not 'value'. The description partially compensates by indicating 'value' contains a status (e.g., Accepted, todo, done), giving the parameter context. However, it doesn't specify constraints like case sensitivity or allowed formats, and the 50% schema coverage leaves room for more.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies the tool as returning rows filtered by a status field, with examples of status values. It distinguishes itself from sibling tools like query_by_id and query_by_kind, though it lacks an explicit verb like 'retrieves'.

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

Usage Guidelines3/5

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

The intended usage is implied by the name and description ('filter by status'), but there is no explicit guidance on when to choose this over alternatives like text_search or cross_ref. No exclusionary conditions are mentioned.

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

refreshA

Reload the served index from disk (requires the refresh scope).

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	bearer token (from TERO_TOKENS)

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden for behavioral disclosure. It mentions the required 'refresh' scope, which is useful, but it does not disclose the side effects of reloading the index, such as whether it replaces the current index, impacts in-flight queries, or what happens if the disk read fails. For a mutation-like tool, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, front-loaded sentence that conveys both the action and the prerequisite. Every word earns its place, with no redundancy or unnecessary detail.

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

Completeness3/5

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

For a simple one-parameter tool, the essential action and scope requirement are covered. However, with no output schema and no behavioral context about the reload's impact, an agent may not know what to expect after invocation or under what circumstances to call it beyond the scope hint. It is adequate but not rich.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%: the only parameter 'token' is fully described as a bearer token from TERO_TOKENS. The description adds no additional parameter semantics, so the baseline score of 3 applies per the calibration guidelines.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses the specific verb 'reload' with the resource 'served index' and source 'from disk', clearly stating what the tool does. This distinguishes it from the sibling query tools (query_by_status, text_search, etc.), which are all read/search operations.

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

Usage Guidelines4/5

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

The description implies usage when the index on disk needs to be reloaded into the served environment, and the scope requirement ('requires the refresh scope') provides a clear prerequisite. It does not explicitly list alternatives or when-not-to-use, but the sibling tools are all query operations, making the refresh action contextually distinct and easy to select when appropriate.

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

text_searchB

Ranked free-text search over id/title/summary.

ParametersJSON Schema

Name	Required	Description	Default
`token`	Yes	bearer token (from TERO_TOKENS)
`value`	Yes	the query text

TDQS

B3.4/5.0

Behavior2/5

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

With no annotations, the description carries the full burden. It only states ranking and search scope but does not disclose authentication requirements, return format, pagination, or side effects. 'Ranked' adds minimal behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that is front-loaded and free of irrelevant detail, making it highly concise.

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

Completeness2/5

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

Without an output schema or annotations, the description should explain return values and usage context. It only states scope and ranking, leaving gaps about response structure, error handling, and token usage. This is incomplete for a search tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds no additional parameter semantics beyond the schema's own descriptions for token and value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs a ranked free-text search over id/title/summary, using a specific verb and resource scope. It distinguishes itself from sibling query_by_* tools which likely use structured queries.

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

Usage Guidelines3/5

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

The description implies usage for free-text search over specific fields but does not explicitly state when to prefer this tool over alternatives like query_by_id or query_by_status, nor any exclusions.

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. Dates show when Glama detected each change.

9 tool updatesv0.2.0Aug 11, 2026
- First observedcite
- First observedcross_ref
- First observedexplain
- First observedidentify
- First observedquery_by_id
- First observedquery_by_kind
- First observedquery_by_status
- First observedrefresh
- First observedtext_search

Install Server

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

TDQS

B3.1/5.0

Disambiguation5/5

Each tool serves a distinct purpose: querying by status, kind, or ID; full-text search; graph traversal; citation retrieval; query analysis; index refresh; and server identity. No two tools overlap in function, making selection unambiguous.

Naming Consistency3/5

Naming follows a mix of patterns: three tools use 'query_by_*', while others use bare verbs (cite, explain, refresh, identify) or compound nouns (cross_ref, text_search). The snake_case convention is consistent, but the verb/noun order varies.

Tool Count5/5

With 9 tools, the server is well-scoped for a query-focused corpus service. Each tool earns its place, covering search, lookup, analysis, and maintenance without unnecessary bulk.

Completeness4/5

Core corpus operations are covered: retrieval by ID, status, kind, text search, and dependency traversal. Minor gaps exist, such as a dedicated tool to list all statuses or kinds, but the existing tools allow agents to work around this.

Maintenance

ActivitySlowing

ResponsivenessNo issues

Resources

GitHub Repository

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tero-mcp-lite

What it is / isn't

Install

Register in .mcp.json (persistent use in this repo)

Auth

Generating an index for any repo

Matching the Rust server

Why a minimal implementation instead of the official mcp Python SDK

Adding a new tool (the registry pattern)

Tests

Framework — remaining tasks

Contact

License

Status & roadmap

Semver + Releases

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

Register in `.mcp.json` (persistent use in this repo)

Why a minimal implementation instead of the official `mcp` Python SDK