Skip to main content
Glama
nlebovits

parseltongue-claude

by nlebovits

parseltongue_load_dsl

Load Parseltongue DSL into a session to parse facts and axioms, returning verification status and flagging quote mismatches (hallucinations).

Instructions

Load Parseltongue DSL into the session.

Parses facts, axioms, defterms, derives, and diffs. Returns verification status including any quote mismatches (hallucinations).

Args: session_id: Session ID dsl_source: Parseltongue DSL source code

Returns: JSON with parse status and verification results

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dsl_sourceYes
session_idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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 does disclose that the tool returns 'verification status including any quote mismatches (hallucinations)', which is useful behavioral context about what the tool checks. However, it doesn't state whether loading replaces existing session content, appends to it, or requires a valid session to exist. For a mutation-like operation with zero annotations, more behavioral disclosure would be expected.

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

Conciseness4/5

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

The description is reasonably structured with a summary line, a list of parsed content types, and an Args/Returns section. The content list (facts, axioms, defterms, derives, diffs) is useful and compact. Minor redundancy exists since Args/Returns restate what parameters and output schema already convey, but the structure is efficient.

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 parses complex DSL content, returns verification results including hallucination detection, and has siblings covering sessions, documents, and consistency checking. An output schema exists (so return format isn't the description's burden), but given the complexity of DSL loading and the 0% schema coverage, the description could explain what 'quote mismatches' mean, how loading interacts with existing session state, and why this differs from register_document. It's minimally adequate.

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 0%, so the description must compensate. It does enumerate the two parameters (session_id and dsl_source) with brief explanations ('Session ID' and 'Parseltongue DSL source code'). However, it adds no format details—DSL source syntax, expected size, encoding, or version constraints are all absent. The explanation of dsl_source as 'Parseltongue DSL source code' is somewhat tautological for someone unfamiliar with the DSL.

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 verb ('Load Parseltongue DSL into the session') and specifies the resource (DSL content) with what it parses (facts, axioms, defterms, derives, diffs). It distinguishes from siblings like create_session and register_document since it's specifically about loading DSL source. Could be slightly stronger in naming the session target explicitly, but the core purpose is 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?

The description provides no guidance on when to use this tool versus siblings like parseltongue_dsl_reference, parseltongue_register_document, or parseltongue_check_consistency. It's ambiguous whether this is the initial ingest step, an update mechanism, or how it relates to session lifecycle. No exclusions or alternative recommendations are provided.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/nlebovits/parseltongue-claude'

If you have feedback or need assistance with the MCP directory API, please join our Discord server