Public-record facts for a GitHub login, or that login's unique public GitHub name, from research findings. Known set is small (canonical maintainers). Most logins return found=false. Do not invent employment or affiliation.
| Name | Required | Description | Default |
|---|---|---|---|
| login | Yes | GitHub login or unique public GitHub name, case-insensitive. Unknown values return found=false. |
Changes observed during successful MCP inspections.
Input schema / properties / login / descriptionPrevious value: -"GitHub login, case-insensitive. Unknown logins return found=false."New value: +"GitHub login or unique public GitHub name, case-insensitive. Unknown values return found=false."Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds valuable behavioral guidance beyond annotations: negative results are common (found=false) and the agent must not invent employment or affiliation. 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is compact and each sentence earns its place: scope, dataset size, negative-result frequency, and anti-fabrication rule. However, the first sentence is structurally confusing and could be clearer without adding length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read-only lookup, the description covers input semantics and a common failure mode, but with no output schema it only vaguely promises 'facts' and does not enumerate likely fields. An agent cannot fully predict the response shape, and the wording leaves ambiguity about whether output is facts or a name.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 100% coverage, including case-insensitivity and the found=false behavior. The description's mention of 'unique public GitHub name' partially overlaps with the schema's parameter description, adding little new meaning. Baseline 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies a specific resource (GitHub login or unique public GitHub name) and what is returned (public-record facts from a curated research set). It is not vague, but the first sentence is grammatically awkward ('or that login's unique public GitHub name') and does not explicitly distinguish this tool from its analysis/search siblings.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives useful context: the known set is small (canonical maintainers) and most logins return found=false, implying this is a narrow lookup rather than a general-purpose search. However, it does not state when to prefer an alternative tool or what to do after a not-found result.
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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