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add-license-information-to-codebase

Automatically prepend license text to multiple codebase files to ensure legal compliance across your project. This tool takes a license string and a list of file paths/contents, returning the updated files with a properly formatted blank line separating the license from your original code. (reference price: $0.0000 per call)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
payloadNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

B3/5.0
Behavior3/5

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

With no annotations provided, the description must disclose behavioral impacts. It mentions that the tool returns updated files with a blank line separating license from code, and lists a 'reference price'. However, it does not disclose whether the operation is destructive (e.g., overwriting original files), reversible, or requires permissions. The absence of destructiveHint or readOnlyHint annotations means the description's partial disclosure is acceptable but incomplete.

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 two sentences long and front-loaded with the primary action. The second sentence adds operational detail (blank line behavior) and pricing. While the pricing note might be extraneous for many use cases, it does not detract significantly. No redundant information is present.

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?

Given the tool's complexity (1 parameter with 0% schema coverage, no annotations, but has an output schema), the description covers the basic purpose and one behavioral detail (blank line formatting). However, it does not explain the output structure, error conditions, or file handling behavior. The presence of an output schema partially compensates, but the description should provide enough context for safe invocation without guessing.

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?

The schema description coverage is 0% for the single parameter 'payload', which is a generic anyOf type with no properties defined. The description adds value by indicating that the tool expects a license string and a list of file paths/contents, but it does not map these to the payload structure. This leaves the agent without concrete guidance on how to structure the input, which is a significant gap.

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's purpose: to prepend license text to multiple codebase files. It uses specific verbs ('prepend license text') and mentions the resource ('codebase files'). However, it does not explicitly distinguish itself from siblings like 'codebase-learning-friction' or 'ai-generated-code-debugging-overhead', which are unrelated but could cause confusion without differentiation.

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 lacks any guidance on when to use this tool versus alternatives. It does not mention prerequisites (e.g., valid license string format) or contexts where it should not be used (e.g., non-code files). The sibling tools include several unrelated utilities, but no exclusions or alternatives are provided.

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

C2.1/5.0
Disambiguation2/5

Many tools have vague or overlapping descriptions, such as multiple 'repetitive task that could be automated' tools that lack clear differentiation. The inclusion of meta-tools (e.g., research_pain_points, develop_tools) alongside domain-specific tools further blurs boundaries, making it hard for an agent to select the correct tool.

Naming Consistency2/5

Tool names use a mix of hyphens (add-license-information-to-codebase) and underscores (develop_tools, check_tool_health), with no consistent pattern. Some names are verbose and descriptive, while others are terse, creating an inconsistent naming convention across the set.

Tool Count3/5

At 20 tools, the count is borderline but not extreme. However, the set includes several tools that are purely descriptive of problems (e.g., ai-generated-code-debugging-overhead) or are meta-tools for the factory itself, which inflates the count without adding practical utility for end users.

Completeness2/5

The server's purpose is unclear, mixing codebase operations, documentation, gamification, and support tickets. There are obvious gaps: no tool for updating or deleting, and the meta-tools (research, develop, health) are not exposed as a coherent lifecycle. The surface feels incomplete for any single domain.

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