Rewrite text to match an instruction — change tone, formality, length, or reading level ("make it formal", "simplify for a 10-year-old") with deterministic output. $0.005/call via x402.
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | Text to rewrite (truncated to 8,000 characters) | |
| instruction | Yes | Rewrite instruction, e.g. "make it formal" |
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?
With no annotations provided, the description takes on the full burden of disclosing behavior. It adds useful details: deterministic output and cost ($0.005/call). It does not mention output format or error handling, but for a simple tool this is sufficient.
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, front-loaded sentence that states the purpose, gives examples, and notes cost and determinism without any waste.
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?
Given the tool's simplicity (2 params, full schema coverage, no output schema), the description provides a complete picture: what it does, examples, and key behavioral traits. It could mention return value explicitly, but that is implied by 'rewrite'.
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 already documents both parameters with descriptions (100% coverage), and the description enriches understanding by providing concrete instruction examples and the range of transformations (tone, formality, length, reading level), adding value 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 clearly states the tool's function with a specific verb ('Rewrite text') and defines the scope ('to match an instruction'), including examples of transformations. It distinguishes itself from sibling tools like summarize_text and classify_text by focusing on rewriting rather than summarization or classification.
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 clear context for when to use the tool: whenever text needs rewriting to match an instruction. However, it does not explicitly mention alternatives or exclusions, but the examples imply specific use cases, so it meets the 'clear context, no exclusions' level.
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 tool groups are nearly indistinguishable: wallet_analyze, wallet_spy, and base_wallet_profile all inspect wallets; batch_extract, batch_url_json, and x401_batch_extract all batch-extract URLs; route_task, agentcore_route, and mpp_route all perform routing. An agent would need to read very carefully to avoid selecting the wrong tool.
All names are snake_case, but there is no consistent verb_noun or namespace pattern: many are noun-only (inference, echo, sentiment, server_time), some are prefixed by domain (bazaar_, base_, x402_, rep_), and action prefixes vary widely (fetch_, compile_, extract_, purchase_, route_). The naming is readable but not predictable across the set.
Seventy tools is an extremely large surface for an agent to choose from, and most appear to be independent paid service wrappers. This exceeds the 50+ extreme mismatch threshold in the calibration and creates an overwhelming selection problem.
Relative to its apparent purpose—exposing x402 payments and Bazaar market data—the coverage is extensive: diagnostics, preflight, settlement verification, receipt lookup, wallet checks, Bazaar analytics, web extraction, and text processing are all represented. The main gaps are operational side-effects like creating or updating a Bazaar listing, but those appear to be outside this read/purchase surface.