search_packagesSearch pub.dev for Dart & Flutter packages by keyword. Returns matching package names and a next-page URL.
| Name | Required | Description | Default |
|---|---|---|---|
| page | No | Result page (default 1). | |
| query | Yes | Search terms, e.g. "state management" or "http client". |
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 establish read-only/idempotent/non-destructive behavior, so the description need not restate safety. It adds meaningful context by naming pub.dev as the source and disclosing that results include matching names and a next-page URL.
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?
Two sentences with no filler. The search target and input framing appear first, and the return summary follows immediately.
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 two-parameter external search tool with full schema coverage and read-only annotations, the description covers source, behavior, and return content. No output schema exists, so the explicit mention of package names and next-page URL fills that gap.
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 coverage is 100%: query and page are both described, including default page. The description's only parameter-related contribution is the general keyword framing, which does not raise it above the baseline.
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 identifies a search over pub.dev for Dart & Flutter packages by keyword, which is a specific verb+resource. It does not explicitly contrast with sibling tools like get_package or list_versions, but the search-vs-retrieve distinction is reasonably evident.
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 use case is implied by 'by keyword': choose this tool when the agent needs to discover packages rather than fetch a known package. It offers no explicit guidance about when not to use it or which sibling (get_package, list_versions) to prefer for package details.
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.
Several tools have clear functional boundaries, but there is meaningful overlap at the top level: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded share one routing pipeline, and ask_pipeworx_beta is currently identical to ask_pipeworx. The six polymarket_* tools also form a dense family where an agent must read long descriptions to distinguish arbitrage scanning from edge detection from fill-risk evaluation.
Names are uniformly lowercase snake_case and mostly follow verb_noun or domain-prefix conventions, which makes the set much more predictable than its count suggests. Minor deviations exist: ask_pipeworx variants are product-noun phrases, polymarket_edges is a noun phrase rather than a verb-led tool, and pairs like polymarket_edges vs polymarket_edge_tracker are easy to misread.
With 31 tools, this server is above the 25+ threshold and feels overloaded for a single MCP surface. It mixes broad data research, prediction-market analysis, memory, subscriptions, feedback, and niche utilities like generate_llms_txt, so the set is more like several related servers merged together.
The core workflow is well covered: discovery, single-answer routing, grounded verification, deep research, entity resolution, comparison, change tracking, subscription lifecycle, and even memory primitives. Missing are minor lifecycle refinements such as updating an existing subscription, and the number of overlapping entry points makes it slightly harder to guarantee the agent will always choose the intended path.