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Northern Forge MCP

hmac_verify

Verify a webhook HMAC signature (GitHub / Stripe / Slack / raw schemes). Computes the expected digest, compares in constant time, and checks the replay window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
algoNosha256 (default) | sha1 | sha512
schemeNoraw | github | stripe | slack (default raw)
secretYesShared signing secret
payloadNoRaw request body, byte-for-byte
signatureNoSignature header to check (optional; omit to just compute the expected value)
timestampNoUnix seconds or ISO time — required for stripe/slack
tolerance_secondsNoReplay window (default 300)

TDQS

A4/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral disclosure burden. It states it 'computes the expected digest, compares in constant time, and checks the replay window,' which are key behavioral aspects. However, it omits details like error handling, whether secret is required, and exact scheme differences, but overall it is reasonably transparent.

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 two sentences, front-loaded with purpose, and every sentence adds value. It avoids verbosity and clearly explains the core behavior without unnecessary details.

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 7-parameter tool with no output schema, the description conveys the main purpose and key behaviors (constant-time compare, replay window). While it doesn't detail each parameter, the schema already covers that. It is sufficient for an agent to decide when to use it, but could mention potential caveats like timestamp formats or default tolerances.

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%, so the description doesn't need to repeat parameter details. It adds some context (e.g., constant-time comparison, replay window) but does not elaborate on individual parameters (like timestamp format), leaving schema to carry most of the meaning.

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 purpose: 'Verify a webhook HMAC signature' with specific schemes (GitHub, Stripe, Slack, raw). It distinguishes from sibling tools like hash_text (which hashes) and jwt_decode (which decodes), so the agent knows exactly when to use this tool.

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 webhook signature verification, but does not explicitly mention when to use it versus alternatives (e.g., hash_text for plain hashing). It lacks explicit 'use when' or 'do not use' guidance, leaving some ambiguity about boundary cases.

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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TDQS

B3.3/5.0
Disambiguation3/5

Most tools are clearly distinct, but there is overlap between local ops tools (gbrain_get/list/search, host_memory_get/set, forge_loop_status, list_mesh_snapshot, etc.) and public status/product tools (forge_status, popular_tools) which could confuse an agent. Descriptions help by tagging local ops, but the boundaries remain blurred.

Naming Consistency4/5

All tool names use snake_case and mostly follow a verb_noun or noun_verb pattern (e.g., get_product, list_live_products, csv_to_markdown). A few names like now_iso and lorem_ipsum deviate from the verb-first style, but overall the naming is predictable and consistent.

Tool Count2/5

With 40 tools, the server is overloaded for a coherent set. Many are simple utility functions that could be consolidated, and the mix of generic utilities, product APIs, and local ops adds unnecessary bulk, pushing the count well above the comfortable range.

Completeness2/5

The server lacks a clear domain, making it impossible to assess lifecycle coverage. While it offers many utilities, there is no coherent surface—missing common operations for any single category (e.g., no CRUD, no file handling) and many tools feel randomly assembled rather than forming a complete workflow.

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