Simulate how a project's architecture behaves under a scenario (load spike, node loss, cache cold, LLM saturation). Returns latency percentiles per load and bottleneck analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| scenario | Yes | Scenario to apply. | |
| project_id | Yes | Project to simulate. |
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 declare readOnlyHint=true, and the description adds meaningful behavioral context: the tool returns latency percentiles per load and performs bottleneck analysis. It does not contradict the read-only hint, and for a simulation tool it adequately discloses what the agent should expect as output.
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?
A single, front-loaded sentence states the action, the resource, the scenario choices, and the return value with no filler. Every part of the sentence earns its place.
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?
With two fully schema-described parameters and no output schema, the description gives enough context: what the tool does, what scenarios it supports, and what it returns. It could mention output format or units, and it does not elaborate on simulation cost or limitations, but for the given complexity this is sufficient.
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?
Schema description coverage is 100% with both parameters documented and enum-restricted, so the schema already explains the parameters well. The description adds scenario examples and output context, but does not provide new parameter-level semantics beyond the schema.
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 uses a specific verb ('Simulate') and resource ('a project's architecture'), then scopes the behavior with concrete scenario examples and output types. It is clearly distinguishable from the sibling read/analysis tools, which are about fetching data or verifying claims rather than running a simulation.
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 implies when to use it: when you need to understand architectural behavior under load spike, node loss, cache cold, or LLM saturation. It gives the scenario categories and the type of answer produced, but it does not explicitly state when not to use it or mention alternatives; there is, however, no obvious sibling that competes for the same task.
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.