perplexity-agent-mcp
This server provides MCP tools for Perplexity's Agent API, enabling multi-step web research with cited answers. You can start research (parameterized by question, depth preset, recency filter, domain allow/deny, and optional non-blocking mode), collect results via response_id, and cancel runs. Features include: selecting research thoroughness (fast to wide-research), filtering sources by publication recency and domain, running parallel queries with non-blocking starts, configurable wait timeout, automatic MCP protocol adaptation, optional llm CLI plugin, and a zero-dependency Python implementation.
Provides tools for interacting with Perplexity's Agent API, enabling AI agents to start multi-step research runs, check their status, and cancel them, returning synthesized answers with citations.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@perplexity-agent-mcpResearch the current state of peace talks between Ukraine and Russia"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
perplexity-agent-mcp
A single-file, zero-third-party-dependency MCP server for Perplexity's Agent API — multi-step web research with citations, exposed to any MCP client over stdio.
Python standard library only: no MCP SDK, no HTTP library, no code generated at build time. perplexity_agent_mcp.py holds your API key and talks to the network on your behalf, so it is written to be read — 1,774 lines, comments included. Both install paths below ship the exact same file; which one you pick changes how many other parties you're trusting to get it onto your disk, not what actually runs.
Background reading: perplexity-agent-mcp: an open-source MCP server for Perplexity's Agent API — why this exists, what the Agent API does that Sonar doesn't, and what building it turned up.
Why this exists
Perplexity's own @perplexity-ai/mcp-server wraps the Sonar chat models and the Search API. It does not reach the Agent API (POST /v1/agent) — Perplexity's multi-step, tool-using research endpoint, which runs its own web searches, fetches pages, and synthesizes one cited answer. This server fills exactly that gap. It's meant to run alongside the official one, not replace it.
Related MCP server: perplexity-web-mcp
The three tools
The Agent API is asynchronous under the hood (see Configuration for why that matters), so this server exposes it as one lifecycle, not one call:
Tool | Does | Use it when |
| Starts a research run. By default, blocks until the answer is ready and returns synthesized, sourced text. | Your default entry point for any research question. |
| Collects a run started earlier — a one-shot progress check, or a blocking wait. |
|
| Asks Perplexity to stop a run that's no longer needed. | You started something with |
perplexity_agent
Param | Type | Default | Notes |
| string | (required) | The research question. |
| string |
|
|
| string | (none) | One of |
| string[] | (none) | Up to 20 domains. Prefix an entry with |
| boolean |
| Block until done, or return a |
perplexity_agent_result
Param | Type | Default | Notes |
| string | (required) | From |
| integer |
|
|
perplexity_agent_cancel
Param | Type | Default | Notes |
| string | (required) | From |
One deliberate asymmetry, visible in tools/list: perplexity_agent's readOnlyHint annotation is false, even though it never touches your local machine — it creates real, billable, cancellable state on Perplexity's servers, and MCP clients use that hint to decide whether a call needs your approval before running. perplexity_agent_cancel carries destructiveHint: true, honestly: it ends a run, and Perplexity reports no usage at all for cancelled runs, so this tool cannot tell you whether cancelling changed your bill (see Security).
Install — Path A: single file
The whole point of this project is that you can read the one file that will hold your API key before you trust it with one. Do that first: download perplexity_agent_mcp.py and read it top to bottom.
Then check your interpreter. Stock macOS ships Python 3.9.6 at /usr/bin/python3 (confirmed on a current macOS install) — below this server's 3.10 floor. It will refuse to start and say why rather than fail obscurely, but you still want a real 3.10+ python3 (Homebrew, python.org, or uv python install all provide one):
python3 --version # must be >= 3.10
which python3 # note this path for the config belowPoint your MCP client at the absolute paths of both the interpreter and the file:
{
"mcpServers": {
"perplexity-agent": {
"command": "/absolute/path/to/python3",
"args": ["/absolute/path/to/perplexity_agent_mcp.py"],
"env": { "PERPLEXITY_API_KEY": "pplx-…" }
}
}
}Use absolute paths for both — the same reason Path B insists on one below applies here too: a GUI app's PATH is not your shell's PATH, and a bare "command": "python3" can quietly resolve to that too-old system interpreter instead of the one you meant. On macOS, Claude Desktop's config file lives at ~/Library/Application Support/Claude/claude_desktop_config.json.
Trust chain: Python standard library, plus Perplexity. Nothing else.
Install — Path B: uvx from PyPI
One JSON snippet, nothing to download by hand. uv fetches and runs the published package each time your MCP client starts the server.
{
"mcpServers": {
"perplexity-agent": {
"command": "/absolute/path/to/uvx",
"args": ["perplexity-agent-mcp"],
"env": { "PERPLEXITY_API_KEY": "pplx-…" }
}
}
}Two things worth getting right, each the difference between a working config and a support thread:
Use an absolute path to
uvx. macOS GUI apps — Claude Desktop launched from Finder or Spotlight, not a terminal — do not inherit your shell'sPATH. If the config above says"command": "uvx", Claude Desktop very likely can't find it and fails withspawn uvx ENOENT. Runwhich uvxin your terminal and paste the absolute path it prints intocommandinstead.
Pin the version if you want reproducibility.
"args": ["perplexity-agent-mcp@0.4.0"]holds you on one release. Unpinned,uvresolves the newest published release on every restart — which is a materially different risk from tracking a branch: releases are immutable, tagged, and go through the same CI as everything else. Pin if you would rather review each upgrade; leave it off if you would rather get fixes automatically. Either is defensible, which is why this is not a warning.
Also: a plugin for llm
The same Perplexity Agent client, exposed as a model for Simon Willison's
llm CLI. Optional — the MCP server never imports
it and keeps its zero dependencies either way.
llm install llm-perplexity-agent
llm keys set perplexity # skip if you already set this for llm-perplexity
llm -m perplexity-agent 'What changed in MCP 2026-07-28?'Options mirror the MCP tool:
llm -m perplexity-agent -o preset xhigh -o recency week 'Latest on X'
llm -m perplexity-agent -o domains 'nasa.gov,-reddit.com' 'Artemis status'
llm -m perplexity-agent -o timeout 600 'Something genuinely deep'Poll progress goes to stderr, so the answer pipes cleanly:
$ llm -m perplexity-agent 'What is MCP?' > answer.md
[perplexity-agent] status queued after 1s; no intermediate results yet
[perplexity-agent] status in_progress after 3s; 10 search result(s) gatheredWhy not just point llm at the MCP server? llm has no MCP support —
simonw/llm#696 has been open since
January 2025. Third-party bridges exist, but the maintained ones pull in the
full MCP SDK to talk to a server that deliberately has no dependencies. And
the existing llm-perplexity
plugin wraps the older Sonar chat models, not the Agent API — the same gap
this project fills for MCP.
One deliberate difference from the MCP server: spotlighting is off by
default here. In MCP the answer goes straight into a model that is holding
tools, so injected instructions could cause actions, and the wrapper is
essential. On the command line the answer goes to a terminal for a human to
read, and llm runs no tool loop by default — so the realistic risk is a
manipulated summary if you pipe it into another model, not a hijacked agent.
Turn it on with -o spotlight true when the output is headed somewhere that
matters:
llm -m perplexity-agent -o spotlight true 'Research X' | llm -m gpt-5 'Summarise'Trust chain: Path A vs Path B
These are not two equally convenient ways to install the same thing. Path B has a strictly larger trust surface than Path A — full stop; that's not a knock on Path B, it's the trade you make for not downloading anything by hand.
Path A: single file | Path B: | |
You trust | Python standard library, Perplexity | Python standard library, Perplexity, plus |
What runs | Exactly the bytes you read | A wheel |
Best for | Maximum auditability | Maximum convenience |
Both paths ship the same bytes — flit_core copies the one .py file into a wheel; nothing is generated, bundled, or vendored in between. Reading the file audits both.
Configuration
Variable | Required | Default | What it does |
| Yes | — | Your Perplexity API key. Missing or empty → a clean tool error naming the variable, never a crash. |
| No |
| Seconds a blocking tool call may wait before handing back a |
Why 55
Claude Desktop enforces a tool-call timeout of 60 seconds that its users cannot configure — no setting, no environment variable, nothing in the UI. A synchronous MCP call that runs longer simply dies from the client's point of view, while Perplexity keeps working — and billing — server-side regardless. 55 leaves this server's own submit-then-poll round trip a 5-second margin inside that hard ceiling.
If your client isn't Claude Desktop, raise it — there's no reason to hand back a response_id early if your client is happy to keep waiting:
"env": { "PERPLEXITY_API_KEY": "pplx-…", "PERPLEXITY_AGENT_WAIT_SECONDS": "300" }300 is what we recommend for Claude Code, VS Code, and Cursor IDE — see why in the table below. An unset, empty, non-numeric, zero, or negative value silently falls back to 55; a valid positive value is capped at 1800 (30 minutes) so a stray extra zero can't hang a call indefinitely. perplexity_agent_result's own wait_seconds parameter advertises this same ceiling as its declared maximum in tools/list — but sending it a larger value anyway doesn't get rejected, it gets silently clamped to the ceiling.
No MCP client publishes Claude Desktop's figure anywhere; we obtained it by reverse-engineering the installed app (v1.24012.1) — its bundled TypeScript SDK defaults DEFAULT_REQUEST_TIMEOUT_MSEC to 60000ms, and Claude Desktop calls callTool() without overriding it. The rest of this table comes from each project's own docs or source, with confidence noted where it's shaky:
Client | Tool-call timeout | User-configurable? | Sends a progress token? |
Claude Desktop | 60 s | No | Never |
Claude Code (stdio) | ~28 h wall-clock, 30 min idle | Yes ( | Yes — auto-backgrounds any call past 2 min |
Cursor (ACP/CLI) | 60 s | No | — |
Cursor (IDE) | ~60 min (one staff forum post, unverified) | No | — |
VS Code / GitHub Copilot | None — waits indefinitely | n/a | Yes, shown in the UI |
MCP TypeScript SDK | 60 s | Per-request | Only if the integrator opts in |
MCP Python SDK | None | Yes | Not implemented |
Progress notifications do not rescue Claude Desktop: the spec lets a client reset its timeout clock when progress arrives, but only if the client supplied a progressToken in the first place, and Claude Desktop never does. This server's notifications/progress support is opportunistic — pure upside for the other clients, a no-op here.
Protocol revisions
This server speaks both eras of MCP, and works out which one you want from what you send. There is nothing to configure, and no way to get it wrong.
Revision | Era | How a request declares it |
| Modern — stateless, no handshake |
|
| Legacy — | an |
| Legacy | negotiated through |
2026-07-28 went GA on 2026-07-28 and removes the initialize/notifications/initialized handshake entirely: the protocol becomes stateless, every request carries its own protocol version and client capabilities, and servers must implement server/discover. It is the largest breaking change in MCP's history.
Nothing about your setup needs to change. A legacy client sees byte-identical behaviour to before — that is asserted by exact-equality tests, not by intention. As of this writing no MCP client has publicly shipped 2026-07-28 support, so in practice every client still takes the legacy path; the modern one is here so that none of them has to wait for this server when they do.
Two behaviours worth knowing if you are testing against it:
server/discoveris answered even without_meta. On stdio it is the designated backward-compatibility probe, and a client reads the reply to decide which era this server is. Any error there would tell it "legacy server" permanently.A modern request naming a revision we don't speak gets
-32022, carrying the list it should retry with. That is deliberately the opposite ofinitialize, which never errors on version negotiation and downgrades instead — the two rules govern two different code paths and are not in conflict.
Presets
preset selects research depth. It's an open string on Perplexity's side too — their API reference types it as a plain string, not an enum — which is why this server doesn't constrain it either: a preset Perplexity adds after this README is written still works, unmodified. These are the ones documented today, roughly shallowest to deepest:
Preset | Perplexity's stated intent |
| Quick factual lookups, minimal latency |
| Balanced research with current information |
| Multi-step research across several sources |
| Exhaustive coverage |
| Open-ended agentic research |
| Building a large, evidence-backed collection of sources |
The one hard number we have: a single medium run — a three-source comparison query — measured 12.5 seconds and $0.039. That's one sample, not a benchmark; different questions and deeper presets will vary, likely a lot. This server always submits in Perplexity's background mode regardless of preset — poll, don't hold a socket open for the length of a research run — which is also the mode wide-research requires.
See Perplexity's presets guide for the current, authoritative list.
Self-test
No API key needed — none of these calls touches the network. There are two of them because this server speaks two protocol revisions, and they exercise one each.
Legacy (2025-11-25), the handshake era:
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-11-25","capabilities":{},"clientInfo":{"name":"manual","version":"1"}}}' \
'{"jsonrpc":"2.0","method":"notifications/initialized"}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
| python3 perplexity_agent_mcp.pyExpect exactly two JSON lines back: an initialize result, then all three tools from tools/list. Nothing for the notifications/initialized line — the spec forbids replying to notifications, including ones the server doesn't specifically act on.
Modern (2026-07-28), the stateless era:
printf '%s\n' \
'{"jsonrpc":"2.0","id":1,"method":"server/discover"}' \
'{"jsonrpc":"2.0","id":2,"method":"tools/list","params":{"_meta":{"io.modelcontextprotocol/protocolVersion":"2026-07-28","io.modelcontextprotocol/clientCapabilities":{}}}}' \
| python3 perplexity_agent_mcp.pyExpect two lines again: a DiscoverResult listing every revision this server speaks, newest first, then the same three tools — this time carrying resultType, ttlMs, cacheScope, and a _meta naming the server. Note that the first line has no _meta at all: server/discover is answered unconditionally, because on stdio it doubles as the probe a dual-era client uses to find out which era it is talking to.
Security
Three things worth stating plainly rather than leaving implied.
1. Perplexity retains your queries. This server never sends a store parameter, so Perplexity's own default applies. Perplexity documents store: false as hiding a response from later retrieve calls — and this server's entire design is submit-then-poll-later, for every request, regardless of preset. So storage can't meaningfully be off for how this tool works: assume every query and answer is retained on Perplexity's servers, governed by their retention policy, not this project's.
2. Returned web content is a prompt-injection vector. Every tool here feeds text scraped from the live web to a language model. That's inherent to the category, not specific to this server, and most web-search MCP servers don't say so. Mitigation: answers are wrapped in a randomized delimiter — <untrusted-web-content-{16 hex chars}>…</untrusted-web-content-{same}>, 64 bits of entropy from secrets.token_hex(8) — with an explicit "this is data, not instructions" instruction, and any occurrence of the closing tag inside the content itself is stripped first so a hostile page can't forge its own way out. This is spotlighting by delimiting (Microsoft Research, arXiv:2403.14720). It is a mitigation, not a fix: no client is obliged to honour the delimiter, and no model is guaranteed to respect it.
3. Your API key sits in plaintext in your MCP client's config file. The MCP specification directs stdio-transport servers to skip its OAuth flow entirely and get credentials from the environment instead, and every MCP client implements that as a plaintext env block in a JSON config file on disk. That's the ecosystem's normal, spec-sanctioned posture for local servers, not a shortcut this project took — but it deserves saying rather than assuming you already know it. Treat that config file like any other secret: correct file permissions, never commit it, never paste it into a support ticket.
This server has no other secret-handling surface worth auditing beyond those three: the key is read from the environment at call time, not import time, so a missing key is a clean tool error rather than a crash; it lives only inside one function's local scope; it's never logged; and there is deliberately no way to redirect the API host via an environment variable — that would itself be an exfiltration path in a tool whose entire configuration is a user-editable JSON file.
Development
No install step — that's the point:
git clone https://github.com/zalez/perplexity-agent-mcp.git
cd perplexity-agent-mcp
python3 -m unittest discover190 tests, pure standard library — tests/fake_perplexity.py runs a fake Perplexity over http.server, in-process, so nothing real is ever called.
Before committing, install the git hooks once and let them run automatically after that:
uv tool install pre-commit # or: pip install pre-commit / pipx install pre-commit
pre-commit install
pre-commit run --all-files18 hooks: file hygiene, ruff + mypy --strict, gitleaks and zizmor for secret and GitHub Actions security scanning, codespell, and the full test suite. CI runs the same hook set (see ci.yml) plus a matrix across Python 3.10–3.14 and a packaging job that builds a wheel, installs it into a clean environment, and drives the console script over real pipes — a successful build alone doesn't prove the entry point works.
More
Design spec — every decision with its rationale, the places the original brief turned out to be wrong, and the live verification results.
Dual-era MCP — why this server speaks two protocol revisions at once, and the three precedence rules that decide which one your request gets.
Blog post — the story behind the project.
SECURITY.md — threat model, and an honest account of what the prompt-injection mitigation does and does not do.
License
BSD-3-Clause. Copyright (c) 2026, Constantin Gonzalez. See LICENSE.
Available Tools
3 toolsperplexity_agentPerplexity Agent ResearchA
Run a research query through Perplexity's Agent API (multi-step web research with citations). Use for deep or multi-hop questions where a single synthesized, sourced answer is wanted. With wait=true (default) this blocks until the answer is ready; if it takes too long you get a response_id to collect later with perplexity_agent_result.
| Name | Required | Description | Default |
|---|---|---|---|
| wait | No | Block until the answer is ready. Set false to get a response_id immediately — useful for running several deep queries in parallel while you do other work. | |
| query | Yes | The research question. | |
| preset | No | Research depth: fast, low, medium, high, xhigh, wide-research. Deeper takes longer. | medium |
| domains | No | Restrict sources to these domains. Prefix with '-' to exclude. Allowlist or denylist, not both. | |
| recency | No | Only use sources published within this window. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=false and openWorldHint=true, so the description carries less burden. It adds valuable nuance by disclosing the blocking behavior (wait=true) and the timeout fallback that yields a response_id—useful context for an agent deciding how to invoke the tool.
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 compact—two sentences—and front-loaded with the action and primary use case. It includes essential behavioral details without any filler, every 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?
Given the tool's complexity (5 params, no output schema), the description covers purpose, use case, and key blocking behavior. It references the response_id and the result tool, helping the agent understand the workflow. It could be more complete by mentioning cancel behavior or result format, but these are secondary.
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%, so the baseline is 3. The description adds a small nuance for the wait parameter (timeout also yields a response_id) but does not add meaningful detail for preset, domains, or recency beyond what the schema already provides.
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 action with 'Run a research query through Perplexity's Agent API (multi-step web research with citations)'—a specific verb and resource. It also distinguishes itself from siblings by explaining the response_id flow for later collection with perplexity_agent_result.
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 gives explicit context with 'Use for deep or multi-hop questions where a single synthesized, sourced answer is wanted.' It also explains when to set wait=false for parallel queries and mentions the fallback to perplexity_agent_result, though it does not mention perplexity_agent_cancel.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
perplexity_agent_cancelCancel Perplexity ResearchADestructive
Ask Perplexity to stop a research run that is no longer needed. Perplexity does not report usage for cancelled runs, so this cannot tell you whether it changed your bill.
| Name | Required | Description | Default |
|---|---|---|---|
| response_id | Yes | The response_id from perplexity_agent. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark this as destructive, and the description adds the behavioral nuance that Perplexity does not report usage for cancelled runs, so the billing impact is unknown. This goes beyond the structured annotation by explaining a consequence of the operation.
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 short sentences, front-loaded with the action and followed by a relevant caveat. No redundant phrasing.
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 simple one-parameter cancel operation, the description covers the action, the trigger condition, and a key side-effect caveat. It doesn't specify the return value, but no output schema exists and the behavior is sufficiently clear.
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 only parameter (response_id) is already fully described in the schema, so the description doesn't need to add more. It correctly references where the ID comes from, matching 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 ('stop') and resource ('a research run') and clearly distinguishes from sibling tools (perplexity_agent, perplexity_agent_result) by focusing on cancellation. Title 'Cancel Perplexity Research' reinforces the action.
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 states when to use it ('when no longer needed'), giving clear contextual guidance. It does not explicitly name alternatives or exclusions, but the sibling tool names make alternative uses obvious.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
perplexity_agent_resultCollect Perplexity ResearchARead-only
Retrieve the result of a research run started by perplexity_agent. If it is still running, reports what it has done so far and how to check again.
| Name | Required | Description | Default |
|---|---|---|---|
| response_id | Yes | The response_id from perplexity_agent. | |
| wait_seconds | No | Block up to this many seconds waiting for completion. 0 checks once. A larger value is silently CLAMPED to this server's configured wait budget (55s here; set PERPLEXITY_AGENT_WAIT_SECONDS to raise it), never rejected. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the description adds value by mentioning that if the run is still running, it reports progress and how to check again. This is useful context not covered by the annotation alone.
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 two sentences, front-loaded with the primary action 'Retrieve', and contains no redundant or irrelevant information. Every 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?
For a simple retrieval tool with two parameters and no output schema, the description covers the main use case and the pending-state behavior. It doesn't specify the return format, but that is not essential given the tool's simplicity and the lack of an output schema.
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%, with both response_id and wait_seconds having detailed descriptions including clamping behavior. The description adds no extra parameter semantics beyond what the schema already provides.
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 retrieves the result of a research run started by perplexity_agent, which is a specific verb+resource. It distinguishes from the sibling cancel tool and the initiating tool by focusing on result retrieval.
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?
It implies usage after perplexity_agent has started a run and explains the behavior if the run is still running. However, it doesn't explicitly mention when not to use it or directly name alternatives beyond the sibling tool list.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
3 tool updates
v0.1.0- First observed
perplexity_agent - First observed
perplexity_agent_cancel - First observed
perplexity_agent_result
TDQS
Each tool has a distinct role: one starts a research run, one retrieves results, and one cancels. The descriptions clearly differentiate their purposes, leaving no ambiguity.
All tools share the 'perplexity_agent' prefix, but the main tool lacks a verb suffix while the others have '_cancel' and '_result'. This is a minor deviation from a fully consistent verb_noun pattern.
Three tools perfectly cover the core operations for running and managing research queries via the Perplexity Agent API. The count is well-scoped and not excessive.
The tool set covers the full lifecycle: start a run, retrieve results, and cancel if needed. There are no obvious missing operations for this domain.
Maintenance
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