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MarkovianProtocol

markovian-mcp

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by MarkovianProtocol
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Python
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markovian-mcp

Bitcoin-anchored, independently verifiable provenance for AI outputs, exposed as MCP tools.

Trust the emitter? No. Prove the emitter. Commit an output to the Markovian chain the moment it is produced, and anyone can later confirm it existed then and has not been altered since, without trusting you, us, or any operator.

A stamp proves provenance, not truth. It proves the data was committed at a time. It does not assert the data is correct.

Two doors, one server

This package is the stdio door. The hosted HTTP door is the same server, with the same tool names, the same parameters and the same returned objects:

https://api.markovianprotocol.com/mcp/

Code written against one works unchanged against the other. Pick a transport, not a semantics. (The trailing slash matters: /mcp returns a redirect that some clients will not follow.)

Related MCP server: Mark Reynolds

Tools

  • markovian_notarize(path? | sha256?) — notarize any file or hash into the public transparency log (7 independent witness cosignatures, hourly Bitcoin anchoring). Hash-only: a path is hashed locally and never uploaded; only 32 bytes reach the log. The receipt carries the leaf index, inclusion-proof URL, an offline c2sp.org/tlog-proof bundle URL, and the witness trust policy. Rate-limited (10/hour).

  • markovian_stamp(data, wallet?, label?, derived_from?) — commit data to the Markovian chain and get back a verifiable, tamper-evident stamp (markovian-provenance/v1). Only the SHA-256 of your data is sent; the raw data is never stored. No wallet or account is needed, the first stamp just works. The returned merkle_root is the handle.

  • markovian_verify(merkle_root) — check a stamp against the public verifier. No key, no account. An unknown or edited root returns verified: false. Anyone can run it, including a party who does not trust the stamper.

  • markovian_trace(merkle_root) — walk a stamp's derived_from lineage. Returns a map of the graph, not a verdict: every node carries hash_binds and anchored, every edge carries edge_verified, and valid is true only if all of them check out.

Lineage links are bound inside the committed bytes, so an edge cannot be added or removed after the fact without changing the root.

Install

pip install markovian-mcp

Configure (Claude Desktop / Cursor / Claude Code)

Local, over stdio:

{
  "mcpServers": {
    "markovian": {
      "command": "markovian-mcp"
    }
  }
}

Or point at the hosted server instead, with no install:

{
  "mcpServers": {
    "markovian": {
      "url": "https://api.markovianprotocol.com/mcp/"
    }
  }
}

Verify without trusting us

Verification is a plain public GET. Nothing in it depends on this package, this server, or our good behaviour:

curl https://api.markovianprotocol.com/verify/<merkle_root>

Change one byte of the stamped data and the root no longer matches. That is the whole guarantee, and it is checkable by someone who thinks we are lying.

More at markovianprotocol.com.

Changelog

0.2.0 — breaking. The tools now speak to the live chain and match the hosted server exactly. Previously this package shipped local-only hash helpers whose names collided with the hosted server's but whose signatures and semantics did not (markovian_stamp(content) returning a locally computed digest, versus markovian_stamp(data) returning a chain commitment). An agent could not move between the two. There is now one set of tools with one meaning. The old canonicalization field also claimed RFC 8785 (JCS) while implementing sorted-key json.dumps, which is not JCS; that claim is gone rather than restated.

Apache-2.0.

Available Tools

3 tools
markovian_stampA

Compute the canonical commitment root for any output.

JSON is canonicalized with RFC 8785 (JCS); other text is used verbatim, then SHA-256 hashed. The returned root is exactly what Markovian anchors to Bitcoin, so anyone can later verify the output was unaltered and existed when claimed, without trusting the source or any operator.

ParametersJSON Schema
NameRequiredDescriptionDefault
contentYes

TDQS

A4.5/5.0
Behavior5/5

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

No annotations provided, but the description fully discloses behavioral traits: JSON canonicalization via RFC 8785, SHA-256 hashing, and Bitcoin anchoring. There is no contradiction with 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?

Two sentences with no wasteful text. The first sentence states the purpose, the second details the process and value proposition.

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?

Given a single string parameter and no output schema, the description covers the input processing, hashing, and the significance for verification, making it complete for the tool's simplicity.

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?

Schema description coverage is 0%, but the description explains how the 'content' parameter is processed (canonicalized if JSON, otherwise verbatim) and the resulting hash, adding meaning beyond the schema's type definition.

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 action ('Compute') and the resource ('canonical commitment root'). It distinguishes the tool from siblings (markovian_trace, markovian_verify) by focusing on commitment creation.

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 creating verifiable commitments, but lacks explicit guidance on when to use versus alternatives (markovian_trace, markovian_verify) or when not to use this tool.

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

markovian_traceA

Walk a lineage of stamps back to its origin.

Pass a JSON array of receipts, each an object with a "root" and an optional "derived_from" (the parent root). Returns the ordered chain from the given head to the origin, so an output can be traced to the data and model it came from.

ParametersJSON Schema
NameRequiredDescriptionDefault
receipts_jsonYes

TDQS

A4.5/5.0
Behavior4/5

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

No annotations provided, so description carries the burden. It discloses the input format (JSON array of receipts) and output (ordered chain). It doesn't mention side effects, but the tool appears to be read-only, which is acceptable.

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?

Two paragraphs, first sentence is a clear header, then details. No wasted words. Well-structured and efficient.

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 tool with one parameter, the description covers input format and output behavior. It lacks explanation of error cases or edge cases, but is otherwise complete.

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?

The description adds significant meaning beyond the schema, explaining that 'receipts_json' must be a JSON array of objects with 'root' and optional 'derived_from'. Schema coverage is 0%, so description compensates fully.

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 walks a lineage of stamps back to its origin, specifying the input format and output. It distinguishes itself from siblings 'markovian_stamp' and 'markovian_verify' by focusing on tracing, not creation or verification.

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 when to use (tracing origin) but does not explicitly state when not to use or mention alternatives. However, given sibling names, the context is clear enough.

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

markovian_verifyA

Verify content against a previously stamped canonical_root by recomputing it.

Tampering with a single byte changes the root and fails the check. Trust nobody: the check is pure recomputation, needing nothing from the operator.

ParametersJSON Schema
NameRequiredDescriptionDefault
contentYes
canonical_rootYes

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: the tool is a pure recomputation with no external dependencies, fails on any byte change, and requires nothing from the operator. This provides clear expectations.

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 concise (two paragraphs, about 40 words) and front-loaded with the purpose. Every sentence adds value without redundancy.

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

Completeness3/5

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

The tool has no output schema, so the description should explain return behavior. It implies failure ('fails the check') but does not state whether it returns a boolean, raises an error, or what happens on success. This is a notable gap given the lack of annotations and schema.

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 description coverage is 0%, so the description must compensate. It mentions 'content' and 'previously stamped canonical_root' but does not specify formats, constraints, or how they relate to each other. The schema titles only provide basic hints.

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 that the tool verifies content by recomputing a canonical root, using specific verbs ('Verify') and resources ('content', 'canonical_root'). It also distinguishes from siblings by focusing on verification, while sibling tools are 'stamp' and 'trace'.

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

Usage Guidelines4/5

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

The description gives context about when to use (after stamping, to detect tampering) and emphasizes that the check is pure and trustless. However, it does not explicitly mention alternatives or when not to use it, leaving some ambiguity.

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.

  1. 3 tool updatesv0.1.0
    • First observedmarkovian_stamp
    • First observedmarkovian_trace
    • First observedmarkovian_verify
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TDQS

A4.4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: stamp computes a canonical root, trace walks lineage, and verify checks content against a root. No overlap.

Naming Consistency5/5

All tools follow a consistent pattern: prefix 'markovian_' plus a verb (stamp, trace, verify). No mixing of styles.

Tool Count5/5

Three tools is well-scoped for the niche domain of Markovian anchoring. Each tool earns its place without duplication.

Completeness4/5

The set covers core operations (stamp, trace, verify). Minor gaps exist, such as no tool to list or search stamps, but the main workflow is complete.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

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