tw-market-data

Get an inclusion proof

get_inclusion_proof

Read-onlyIdempotent

Prove a row was in the snapshot TWMD published — and check it yourself.

Returns the Merkle sibling path, the signed root, and the checkpoint it belongs to. It returns
the PATH rather than a yes/no on purpose: a service that answers "yes, it is included, trust me"
is the opposite of verifiable. Recompute the root from the leaf and the path; the verifier is
~30 lines and is written out in docs/VERIFIABLE_DATA.md.

★ WHAT IT PROVES: integrity (the row was not altered after publication) and origin (the root was
signed by TWMD). **It does NOT prove the numbers are correct** — if the exchange published a
wrong figure, TWMD faithfully committed to the wrong figure. Do not present a passing proof as
a correctness guarantee.

★ THREE STATUSES, and they must not be collapsed:
  ok               proof enclosed; verify it.
  not_in_snapshot  that row_key was NOT a leaf of the snapshot. A true answer, NOT a failure and
                   NOT evidence of tampering.
  no_checkpoint    no snapshot was ever built for that dataset/version.

🔴 VERIFY ON THE CLIENT SIDE. There is deliberately NO `verify` tool here, and that is the
point: this server is the ISSUER of the proof, so a "we checked our own proof and it is fine"
tool would carry almost no evidential weight while looking exactly like independent
verification. Run the check where you are:

  * Python SDK   `twmd.Client.flight(..., proof_api=...).verify(dataset, row_key)`
                 -> {"verified": bool, "verification": {status, proves, checkpoint,
                    unverified_reason}} with FOUR values
                    (verified / failed / unproven / absent) — `unproven` is NOT `failed`.
  * any language `scripts/twmd_verify_proof.py` — copy it; stdlib only, imports nothing from
                 our package (a verifier that needs our package asks you to trust our package).
  * the core     `twmd.proof_core` is the SAME SOURCE TEXT as that script, guarded per symbol
                 by tests/test_proof_core_is_one_source.py, so the two cannot drift.

Args:
    dataset: e.g. 'daily_price'. row_key: the dataset's LOGICAL key joined by '|',
    e.g. '2330|2026-08-14'. snapshot_version: omit for the most recent checkpoint.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`dataset`	Yes
`row_key`	Yes
`snapshot_version`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 19, 2026

TDQS

A5/5.0

Behavior5/5

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

The description goes far beyond the annotations: it explains the three possible statuses, clarifies that a passing proof does not mean the underlying numbers are correct, and discloses why no server-side verifier exists. It adds meaningful behavioral context without contradicting the readOnly/openWorld/idempotent/destructive annotations.

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

Conciseness5/5

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

The description is long but well-structured and front-loaded with the core purpose and caveats. Every section—what it proves, statuses, client-side verification, and arguments—earns its place, and the formatting makes the nuanced content easily scannable.

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

Completeness5/5

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

For a three-parameter read-only tool with an output schema and annotations, this description is complete. It covers invocation, interpretation of all statuses, verification workflow, and the proof's limitations, so an agent can call and use the tool correctly without additional context.

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

Parameters5/5

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

Schema description coverage is 0%, but the description fully compensates by giving concrete guidance for all three parameters: dataset example, row_key format using logical keys joined by '|', and snapshot_version omit behavior. This is exactly the kind of semantic detail the schema lacks.

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 states the exact outcome—prove a row was in TWMD's snapshot—and names the concrete return payload (Merkle sibling path, signed root, checkpoint). It also contrasts with a yes/no inclusion service, making the tool's purpose unmistakable and clearly 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.

Usage Guidelines5/5

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

It explicitly says when this tool is used, explains that there is deliberately no server-side verify tool, and directs the agent to client-side alternatives (Python SDK, stdlib script). It also specifies when snapshot_version can be omitted, providing actionable routing and alternatives.

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

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