Get the owner's engineering experiments — hypothesis, command, raw result and verdict (confirmed/refuted/partial) for each. Use it to answer 'what did you measure' or 'show me an experiment you ran'. Includes negative results (approaches that failed). Read-only, closed world.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=false; the description adds useful behavior by noting that experiments include negative results and that verdict values are confirmed/refuted/partial. 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?
All sentences earn their place: the first defines scope and content, the second gives usage cues, and the third clarifies inclusion and read-only/closed-world behavior. No filler.
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 zero-parameter, read-only listing tool with no output schema, the description fully documents what each experiment contains, confirms read-only/closed-world behavior, and includes negative results. An agent can invoke and interpret it without further context.
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 tool has zero parameters, so the baseline is 4. Nothing further is needed or possible.
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 states a specific verb and resource — 'Get the owner's engineering experiments' — and enumerates the fields returned: hypothesis, command, raw result and verdict. It clearly distinguishes this from sibling tools by focusing on experiments rather than articles, issues, or timeline entries.
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?
It explicitly maps to user intents: 'what did you measure' or 'show me an experiment you ran'. It does not name exclusions or alternatives, but the zero-parameter scope makes it reasonably self-contained.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map cleanly to distinct content types such as profile, projects, issues, diary, experiments, and live-source verification. A few pairs like get_projects/search_projects and get_articles/verify_article touch the same subject matter, but their descriptions clarify the intended action well enough for an agent.
All tools follow a consistent snake_case verb_noun pattern: get_* for portfolio content, verify_* for external grounding, plus analyze_stack, search_projects, and simulate_architecture. There is no mixing of conventions or vague generic verbs.
18 tools is on the higher end but justified by the portfolio's breadth: content domains, project search/simulation, and open-world verification all have distinct needs. It is slightly heavy but not bloated; each tool has a discernible reason to exist.
The surface covers the full portfolio/interview domain: profile, projects, timeline, articles, repos, packages, issues, engineering history, experiments, principles, and verification. It also includes grounding against live sources, leaving no obvious dead ends for an agent answering questions about the owner.