csv_to_jsonParse RFC 4180 CSV into JSON (keyless, offline). With header true (default) the first row becomes object keys; otherwise rows are returned as arrays. Handles quoted fields, escaped quotes, and embedded commas/newlines. Set delimiter for TSV etc.
| Name | Required | Description | Default |
|---|---|---|---|
| csv | Yes | The CSV text. | |
| header | No | Treat the first row as a header (default true). | |
| delimiter | No | Field delimiter (default ","). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "csv": "name,age,city\nAlice,30,New York\nBob,25,Los Angeles",
+ "header": true
+ },
+ {
+ "csv": "product\tprice\tquantity\nWidget\t9.99\t100\nGadget\t19.99\t50",
+ "delimiter": "\t"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond annotations (readOnly, idempotent, non-destructive), the description adds critical behavioral details: offline processing, handling of quoted fields, escaped quotes, embedded commas/newlines, and the optional header 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?
Description is three concise sentences, front-loaded with the main purpose and followed by key details. No unnecessary words; every sentence adds value.
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?
Provides enough context for an agent to understand the tool's capabilities and correct usage. Despite no output schema, it describes the output format clearly (JSON with objects or arrays). Handles edge cases and variations.
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 covers all 3 parameters with 100% description coverage. The description adds value by explaining how header affects output (object vs array) and how delimiter works, providing context beyond the schema's brief descriptions.
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 it parses RFC 4180 CSV into JSON, specifying keyless and offline operation. It distinguishes from siblings like json_to_csv, and gives specific behaviors for header and delimiter.
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?
Provides clear usage context: when to use (parsing RFC 4180 CSV), and hints at alternatives by mentioning delimiter for TSV. Does not explicitly state when not to use, but sufficient for typical use.
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 overlap in purpose: ask_pipeworx_beta is explicitly identical to ask_pipeworx today, and the polymarket_* family plus bet_research all touch prediction-market analysis. The descriptions are extremely detailed and mostly disambiguate, but an agent must rely on very long text to avoid misselection.
Names are almost all snake_case and readable, but the pattern is mixed: some are verb-first (resolve_entity, list_subscriptions), some noun-first (entity_profile, polymarket_edges), and some are one-word verbs (remember, forget). The ask_pipeworx_* and recent_* prefixes are consistent, but there is no single verb_noun convention throughout.
33 tools is well over the 25+ threshold, especially for a server named 'Csv' that contains only two CSV-specific tools. The rest spans several unrelated domains: data research, prediction markets, subscriptions, memory, AI visibility, and package scanning. The set feels like multiple servers bundled together rather than one well-scoped surface.
As a broad data-research platform, coverage is strong: lookup, grounded answers, deep research, entity resolution, profiles, comparisons, fact-checking, subscription lifecycle, and memory persistence are all present. Minor gaps exist, such as no direct fetch tool for pipeworx:// citation URIs and no subscription-update tool, but most workflows have no dead ends.