plan
Plan workflow steps for a goal (free).
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes |
Plan workflow steps for a goal (free).
| Name | Required | Description | Default |
|---|---|---|---|
| goal | Yes |
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?
No annotations are provided, so the description must disclose behavioral traits. It only says 'Plan workflow steps for a goal (free).' It does not indicate whether this is a read-only operation, what the output looks like, whether it requires further actions, or any side effects. The '(free)' hint is about cost, not behavior.
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 a single, concise sentence with no filler, which is efficient. However, it is under-specified, failing to include necessary context about usage, output, or parameters. It is not egregiously terse like 'Process', but it falls short of effective communication.
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 tool with one parameter, no output schema, and no annotations, the description should provide enough context to understand its behavior and output. It does not explain what 'plan workflow steps' means in terms of return value, whether it is a one-time generation or a stateful plan, or how it relates to workflow execution tools. The description is largely inadequate.
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 input schema has one parameter 'goal' with no description (0% coverage). The description mentions 'for a goal,' which maps to the parameter and indicates its purpose, but it does not explain expected format, constraints, or examples. This minimal compensation is insufficient for an agent to know how to fill the parameter correctly.
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 clearly states the tool's purpose: 'Plan workflow steps for a goal.' It uses a specific verb (plan) and resource (workflow steps for a goal). However, it does not explicitly distinguish itself from sibling tools like 'run_mission' or 'workflow_stateful', which also deal with workflows or goals.
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 provides no guidance on when to use this tool versus alternatives. It neither mentions specific use cases, prerequisites, nor exclusions. The only hint is 'for a goal,' but there is no comparison with related tools like 'advisor' or 'agent_research'.
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.
Multiple tools have overlapping or identical purposes, such as ocr_url and ocr_image (both OCR from an image URL), compare_texts and text_diff (both compare or diff texts), extract_url and read_url (both extract webpage content), and content_hash and hash_text (both compute hashes). The boundaries between these tools are unclear, causing a high risk of misselection.
Naming conventions are mixed. Many tools use verb_noun (extract_url, validate_email), but others use noun_verb (language_detect, html_clean), single words (advisor, crawl, retrieve), or noun_noun (job_status, page_metadata). This inconsistency makes it harder to predict tool names.
With 100 tools, the server is extremely over-scoped for a generic agent toolkit. While some tools are distinct and useful, the sheer number does not align with a focused purpose; many tools are redundant or highly specialized, and the count exceeds what is typically manageable for an agent to reason about.
The toolkit covers a broad range of utilities including extraction, validation, processing, research, memory, and orchestration. However, there are no CRUD tools for creating/updating/deleting resources, no database or file system operations, and no integration beyond web/API basics. This leaves significant gaps for agents that need general lifecycle management, though it does handle many common tasks.